• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.5
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• pp.342-347
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• 2021

Phase evolution, sintering behavior, microstructure, and microwave dielectric properties of (1-x) mol Ba₃V₄O₁₃ - (x) mol BaV₂O₆ system were investigated. The sintered specimens of all compositions consisted of Ba₃V₄O₁₃ and BaV₂O₆, and no secondary phase was observed. As x increased, the linear shrinkage decreased to the composition of x=0.5, and then increased again, implying that Ba₃V₄O₁₃ and BaV₂O₆ phases interfered mutually with each other during sintering. All compositions showed a dense microstructure with a large grain growth. Cracks were observed in some compositions because of the relatively high sintering temperature of 620~640℃. As x increased, the dielectric constant increased, while the quality factor was maintained from about 50,000 GHz to about 70,000 GHz up to the composition of x=0.9, and then decreased to 20,987~27,180 GHz at the composition of x=1.0. As x increased, the temperature coefficient of the resonance frequency showed a (+) value from a (-) value. The dielectric constant, the quality factor, and the temperature coefficient of resonant frequency of x=0.7 composition sintered at 640℃ for 4 hours were 10.61, 71,126 GHz, and -4.9 ppm/℃, respectively. This composition showed a good chemical compatibility with Al powder, indicating that the Ba₃V₄O₁₃-BaV₂O₆ ceramics are a candidate material for ULTCC (Ultra-Low Temperature Co-fired Ceramics) applications.

• Economic and Environmental Geology
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• v.30 no.3
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• pp.249-262
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• 1997

The Sr and Nd isotopic compositions of two foliated granitic plutons located in the Chonju and Sunchang area were determined in order to reconfirm the intrusion ages of granitoids and to study the sources of granitic magmas. The best defined Rb-Sr isochron for the whole rock samples of the Chonju foliated granite (CFGR) give an age of $284{\pm}12Ma$, suggesting early Permian intrusion age. In contrast, the whole rock Rb-Sr data of the Sunchang foliated granite (SFGR) scatter widely on the isochron diagram with very little variation in the $^{87}Rb/^{86}Sr$ ratios and, therefore, yield no reliable age information. Futhermore they show the concordance of mineral and whole rock Rb-Sr isochron and divide into two linear groups with roughly the same slopes and significantly different $^{87}Sr/^{86}Sr$ ratios, indicating some kind of Rb-Sr distortion in whole rock scale and a difference in source material and/or magmatic evolution between two subsets. The reconstructed isochrons of 243 Ma, which was defined from the proposed data by the omission of one sample point with significantly higher $^{87}Rb/^{86}Sr$ ratio than the others, and 252 Ma, from the combined data of it and some of this study, strongly suggest the possibility that the SFGR was intruded appreciably earlier than had previously been proposed, although the reliability of these ages still questionable owing to high scatter of data points and, therefore, further study is necessary. All mineral isochrons for the investigated granites show the Jurassic to early Cretaceous thermal episode ranging from 160 Ma to 120 Ma Their corresponding initial $^{87}Sr/^{86}Sr$ ratios correlate well with their whole rock data, indicating that the mineral Rb-Sr system of the investigated granites was redistributed by the postmagmatic thermal event during Jurassic to early Cretaceous. The initial ${\varepsilon}Sr$ values for the CFGR (64.27 to 94.81) tend to be significantly lower than those for the SFGR (125.43 to 167.09). Thus it is likely that there is a marked difference in the magma source characteristics between the CFGR and the SFGR, although the possibility of an isotopic resetting event giving rise to a high apparent initial ${\varepsilon}Sr$ in the SFGR can not be ruled out. In contrast to ${\varepsilon}Sr$, both batholiths show a highly resticted and negative values of initial ${\varepsilon}Nd$, which is -14.73 to -19.53 with an average $-16.13{\pm}1.47$ in the CFGR and -14.78 to -18.59 with an average $-17.17{\pm}1.01$ in the SFGR. The highly negative initial ${\varepsilon}Nd$ values in the investigated granitoids strongly suggest that large amounts of recycled old continental components have taken part in their evolution. Furthermore, this highly resticted variation in ${\varepsilon}Nd$ is significant because it requires that the old crustal source material, from which the granitoid-producing melts were generated, should have a reasonably uniform Nd isotopic composition and also quit similar age. Calculated T2DM model ages give an average of $1.83{\pm}0.25Ga$ for CFGR and $1.96{\pm}0.19Ga$ for SFGR, suggesting the importance of a mid-Proterozoic episode for the genesis of two foliated granites. Although it is not possible to determine precisely the source rock compositions for the investigated foliatic granites, the Sr-Nd isotopic evidences indicate that midcrustal or less probably, a lower crustal granulitic source could be the most likely candidate.