• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korean Society of Forest Science
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• v.32 no.1
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• pp.29-35
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• 1976

In order to estimate sawmill productivities, rates of technical change and optimum combination of production factors, Cobb-Douglas production functions have been derived using data obtained from 96 sample mills in Busan-Incheon, southwestern and northeastern areas. The results may be summarized as follows: 1. There is a tendency of expanding average sawmill size in the areas. The horse-power holdings per mill have been increased at the rates of 91 percent in Busan-Incheon, 7.7 percent in southwestern and 16.9 percent in northeastern areas. This implies that the mills around log-importing ports have made rapid development, compared with those in forest regions. 2. The regression coefficients (production elasticities) of the functions for the year of 1967 in the above three areas are much similar each other, but significant differencies are found in the production functions of 1975. In other words, sawmill productivity was mainly restricted by capital deficiencies in all areas in 1967, but this situation was succeeded only by N-E area in 1975. The range of sum of regression coefficients is 1.0437-1.4214, this indicates increasing rates of return to scale. 3. The annual rates of technical changes in B-I, S-W and N-E areas for the observed period are 17.6, 7.6 and 2.2 percents respectively. Busan-Incheon is the only area where labor productivity is higher than that of capital. 4. The best combination of production factors for maximizing firm's profit is subject to the changes of input and output prices. With some assumptions of prices and costs, the optimum levels of power and labor input in B-I, S-W and N-E areas are 57:17, 427:94 and 192:27.

• Korean Journal of Environmental Agriculture
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• v.24 no.3
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• pp.270-279
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• 2005

It is important to understand and evaluate the environmental impacts of rice cultivation for developing environmentally-friendly agriculture because rice is main crop in Korea and rice cultivation have both functions of water pollution and purification with environmental and cultivation conditions. This paper presents the evaluation of nitrogen impact by rice cultivation on water system. A simple protocol was proposed to assess the potential amount of nitrogen outflow from paddy field and most of parameters affect on the nitrogen outflow from paddy field such as the amount of fertilizer application, water balance, the quality and quantity of irrigation water, soil properties, nitrogen turnover in the soil and cultivation method were considered. To develop the protocol, coefficients for parameters affected nitrogen turnover and outflow were gotten and summarized by comparison and analysis of all possible references related, and by additional experiments at field and laboratory. And potential amount of nitrogen input and output by water in paddy field were estimated with the protocol at the conditions of the nitrogen contents of irrigation water, amount of fertilizer application, and irrigation methods. Where irrigation water was clean, below 1.0 mg $L^{-1}$ of nitrogen concentration, rice cultivation polluted nearby watershed. At the conditions of 2.0 mg $L^{-1}$ of nitrogen concentration, 110 kg $ha^{-1}$ of nitrogen fertilizer application and flooding irrigation, rice cultivation had water pollution function, but it had water purification function with intermittent irrigation. At the conditions of 3.0 mg $L^{-1}$ of nitrogen concentration and 110 kg $ha^{-1}$ of nitrogen fertilizer application, rice cultivation had water purification function, but that had water pollution function with 120 kg $ha^{-1}$ of nitrogen application. Where irrigation water was polluted over 6.0 mg $L^{-1}$ of nitrogen, it was evaluated that rice cultivation had water purifying effect, even though the amount of nitrogen application was 120 kg $ha^{-1}$.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.9
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• pp.56-63
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• 1999

Conventional method to improve the response time of the Cat-SPPCM was to increase fanning beam using cylindrical lens. But, in this case the temporal instability of this SPPCM is increased, and fanning beam plays a role as noise, so that it decreases the reflectivity. Thus, fanning beam must be controlled to improve the properties of SPPCM. In this paper, we propose the method to increase the reflectivity of SPPCM and decrease the response time by focusing line-shaped input beam into photorefractive crystal using cylindrical lens and decrease the temporal instability of output beam by using mixed-polarized beam instead of simple extraordinary polarized beam. Optical experiments show that the reflectivity of proposed SPPCM is increased twice and the response time is reduced by 15 times. Also, we observed that the temporal instability of SPPCM is reduced when the polarization angle of the mixed-polarized beam is between $10^{\circ}$ and $30^{\circ}$ . We used a $45^{\circ}-cut\;BaTiO_3$ crystal which has high electro-optical coefficients in optical experiments.

• The Journal of Fisheries Business Administration
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• v.32 no.1
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• pp.15-39
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• 2001

The study, using the input-output analysis of 402 industrial sectors by Bank of Korea(BOK) and the resulting outcomes of price model, aims to evaluate the spillover effects the international fluctuations in crude oil prices have on the commodities prices and consequently, analyse the management and profitability of the offshore fisheries in Korea. At present, the fisher men are provided with tax-free oils for their fishing operations as specified under the Special Tax Treatment Control Law. However, the exhaustion of marine resources and new international fisheries agreements, which resulted in the loss of fishing grounds, made the stable catch even more unpredictable and the hike in the price of the international crude oil would have adverse effects on the fishing industry. The study revealed that the increasing rise in the price of crude oil would exert sweeping spillover effects on other industry sectors in general and accordingly, lead to a poorer performance by fisheries. The price spillover coefficients for the diesel oil was 0.6026, which would translate into the 42.6% increase in the prices of oil when the increase ratio of 73.3% for the base crude oil was applied based on the calculation methods employed in the study. This in turn increased the ratio of diesel oil required in the offshore fisheries from 23.3% to 16.6%, diminishing the ratio of current net profits to minus 2.0% from 4.2% otherwise. By fishing type, the Pair Trawl suffered current net profits loss most by ratio of minus 9.4% and other fisheries such as Coastal Stow Nets, Coastal Angling, Danish Sein also suffered ratio of 7% and more in the loss of current net profits. With the deteriorating fishing performance, coupled with the increasing international crude oil prices, it is urgently required that the authorities concerned deliberate in depth on such schemes as follows in efforts to secure stable fishing production. First, provision of large-scale storage facilities for oil is needed to timely adapt to the fluctuations in international crude oil prices. Secondly, in line with the stabilization of tax-free oil prices, duty levied on oils for fishing and tax collected from the refineries need to be tax-exempt. Thirdly, the beneficiaries from the provision of tax-free oil should be broadened, not limited to special fishing operation only. Fourth, investment in stabilization of the oil prices should be encouraged, possibly through funding from the formation of fisheries development funds underway.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1742-1754
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• 1999

In this paper, we propose a rate control scheme, using histogram based rate-distortion (R-D) estimation, which produces a consistent picture quality between consecutive frames. The histogram based R-D estimation used in our rate control scheme offers a closed-form mathematical model that enable us to predict the bits and the distortion generated from an encoded frame at a given quantization parameter (QP) and vice versa. The most attractive feature of the R-D estimation is low complexity of computing the R-D data because its major operation is just to obtain a histogram or weighted histogram of DCT coefficients from an input picture. Furthermore, it is accurate enough to be applied to the practical video coding. Therefore, the proposed rate control scheme using this R-D estimation model is appropriate for the applications requiring low delay and low complexity, and controls the output bit-rate ad quality accurately. Our rate control scheme ensures that the video buffer do not underflow and overflow by satisfying the buffer constraint and, additionally, prevents quality difference between consecutive frames from exceeding certain level by adopting the distortion constraint. In addition, a consistent considering the maximum tolerance BER of the voice service. Also in Rician fading channel of K=6 and K=10, considering CLP=$10^{-3}$ as a criterion, it is observed that the performance improment of about 3.5 dB and 1.5 dB is obtained, respectively, in terms of $E_b$/$N_o$ by employing the concatenated FEC code with pilot symbols.

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

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 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 Bio-Environment Control
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• v.29 no.3
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• pp.277-284
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• 2020

To increase the utilization of the intelligent methodology of smart farm management, estimation modeling techniques are required to assess prior examination of crops and environment changes in realtime. A mandatory environmental factor such as CO₂ is challenging to establish a reliable estimation model in time domain accounted for indoor agricultural facilities where various correlated variables are highly coupled. Thus, this study was conducted to develop an artificial neural network for reducing time complexity by using environmental information distributed in adjacent areas from a time perspective as input and output variables as CO₂. The environmental factors in the smart farm were continuously measured using measuring devices that integrated sensors through experiments. Modeling 1 predicted by the mean data of the experiment period and modeling 2 predicted by the day-to-day data were constructed to predict the correlation of CO₂. Modeling 2 predicted by the previous day's data learning performed better than Modeling 1 predicted by the 60-day average value. Until 30 days, most of them showed a coefficient of determination between 0.70 and 0.88, and Model 2 was about 0.05 higher. However, after 30 days, the modeling coefficients of both models showed low values below 0.50. According to the modeling approach, comparing and analyzing the values of the determinants showed that data from adjacent time zones were relatively high performance at points requiring prediction rather than a fixed neural network model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.683-694
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• 2002

In this paper, a digital image compression codec using 2D DWT(Discrete Wavelet Transform) is designed using the FPGA technology for real time operation The implemented image compression codec using wavelet decomposition consists of a wavelet kernel part for wavelet filtering process, a quantizer/huffman coder for quantization and huffman encoding of wavelet coefficients, a memory controller for interface with external memories, a input interface to process image pixels from A/D converter, a output interface for reconstructing huffman codes, which has irregular bit size, into 32-bit data having regular size data, a memory-kernel buffer to arrage data for real time process, a PCI interface part, and some modules for setting timing between each modules. Since the memory mapping method which converts read process of column-direction into read process of the row-direction is used, the read process in the vertical-direction wavelet decomposition is very efficiently processed. Global operation of wavelet codec is synchronized with the field signal of A/D converter. The global hardware process pipeline operation as the unit of field and each field and each field operation is classified as decomposition levels of wavelet transform. The implemented hardware used FPGA hardware resource of 11119(45%) LAB and 28352(9％) ESB in FPGA device of APEX20KC EP20k600CB652-7 and mapped into one FPGA without additional external logic. Also it can process 33 frames(66 fields) per second, so real-time image compression is possible.