• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.6
• /
• pp.111-121
• /
• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.1
• /
• pp.28-36
• /
• 2023

Solar radiation that is measured at relatively small number of weather stations is one of key inputs to crop models for estimation of crop productivity. Solar radiation products derived from GK-2A and Himawari 8 satellite data have become available, which would allow for preparation of input data to crop models, especially for assessment of crop productivity under an agrivoltaic system where crop and power can be produced at the same time. The objective of this study was to compare the degree of agreement between the solar radiation products obtained from those satellite data. The sub hourly products for solar radiation were collected to prepare their daily summary for the period from May to October in 2020 during which both satellite products for solar radiation were available. Root mean square error (RMSE) and its normalized error (NRMSE) were determined for daily sum of solar radiation. The cumulative values of solar radiation for the study period were also compared to represent the impact of the errors for those products on crop growth simulations. It was found that the data product from the Himawari 8 satellite tended to have smaller values of RMSE and NRMSE than that from the GK-2A satellite. The Himawari 8 satellite product had smaller errors at a large number of weather stations when the cumulative solar radiation was compared with the measurements. This suggests that the use of Himawari 8 satellite products would cause less uncertainty than that of GK2-A products for estimation of crop yield. This merits further studies to apply the Himawari 8 satellites to estimation of solar power generation as well as crop yield under an agrivoltaic system.

• Proceedings of the Korea Contents Association Conference
• /
• 2003.05a
• /
• pp.344-350
• /
• 2003

In this paper, we propose a novel sharing method ordering the events occurring between users collaborated with the common telecomputing environment. We realize the sharing method with multimedia data to improve the coworking effect using teleprocessing network. This sharing method advances the efficiency of communicating projects such as remote education, tele-conference, and co-authoring of multimedia contents by offering conveniences of presentation, group authoring, common management, and transient event productions of the users. As for the conventional sharing white board system, all the multimedia contents segments should be authored by the exclusive program, and we cannot use any existing contents or program. Moreover we suffer from the problem that ordering error occurs in the teleprocessing operation because we do not have any line-up technology for the input ordering of commands. Therefore we develop a method of retrieving input and output events from the windows system and the message hooking technology which transmits between programs in the operating system In addition, we realize the allocation technology of the processing results for all sharing users of the distributed computing environment without any error. Our sharing technology should contribute to improve the face-to-face coworking efficiency for multimedia contents authoring, common blackboard system in the area of remote educations, and presentation display in visual conference.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.111-131
• /
• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Korea Water Resources Association
• /
• v.49 no.6
• /
• pp.565-577
• /
• 2016

The objective of this study is to propose a method for computing the Natural Drought Index (NDI) that does not consider man-made drought facilities. Principal Component Analysis (PCA) was used to estimate the NDI. Three monthly moving cumulative runoff, soil moisture and precipitation were selected as input data of the NDI during 1977~2012. Observed precipitation data was collected from KMA ASOS (Korea Meteorological Association Automatic Synoptic Observation System), while model-driven runoff and soil moisture from Variable Infiltration Capacity Model (VIC Model) were used. Time series analysis, drought characteristic analysis and spatial analysis were used to assess the utilization of NDI and compare with existing SPI, SRI and SSI. The NDI precisely reflected onset and termination of past drought events with mean absolute error of 0.85 in time series analysis. It explained well duration and inter-arrival time with 1.3 and 1.0 respectively in drought characteristic analysis. Also, the NDI reflected regional drought condition well in spatial analysis. The accuracy rank of drought onset, termination, duration and inter-arrival time was calculated by using NDI, SPI, SRI and SSI. The result showed that NDI is more precise than the others. The NDI overcomes the limitation of univariate drought indices and can be useful for drought analysis as representative measure of different types of drought such as meteorological, hydrological and agricultural droughts.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.25 no.5
• /
• pp.765-772
• /
• 2011

Knowledge which is represented by formal logic are widely used in many domains such like artificial intelligence, information retrieval, e-commerce and so on. And for medical field, medical documentary records retrieval, information systems in hospitals, medical data sharing, remote treatment and expert systems need knowledge representation technology. To retrieve information intellectually and provide advanced information services, systematically controlled mechanism is needed to represent and share knowledge. Importantly, medical expert's knowledge should be represented in a form that is understandable to computers and also to humans to be applied to the medical information system supporting decision making. And it should have a suitable and efficient structure for its own purposes including reasoning, extendability of knowledge, management of data, accuracy of expressions, diversity, and so on. we call it ontology which can be processed with machines. We can use the ontology to represent traditional medicine knowledge in structured and systematic way with visualization, then also it can also be used education materials. Hence, the authors developed an Shanghanlun ontology by way of showing an example, so that we suggested a methodology for ontology development and also a model to structure the traditional medical knowledge. And this result can be used for student to learn Shanghanlun by graphical representation of it's knowledge. We analyzed the text of Shanghanlun to construct relational database including it's original text, symptoms and herb formulars. And then we classified the terms following some criterion, confirmed the structure of the ontology to describe semantic relations between the terms, especially we developed the ontology considering visual representation. The ontology developed in this study provides database showing fomulas, herbs, symptoms, the name of diseases and the text written in Shanghanlun. It's easy to retrieve contents by their semantic relations so that it is convenient to search knowledge of Shanghanlun and to learn it. It can display the related concepts by searching terms and provides expanded information with a simple click. It has some limitations such as standardization problems, short coverage of pattern(證), and error in chinese characters input. But we believe this research can be used for basic foundation to make traditional medicine more structural and systematic, to develop application softwares, and also to applied it in Shanghanlun educations.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.15 no.3
• /
• pp.357-373
• /
• 2013

In this paper, a back analysis program for analyzing the behavior of tunnel-ground system and evaluating the material properties and tunnel design parameters was developed. This program was designed to be able to implement the back analysis of underground structure by combination of using FLAC and optimized algorithm as direct method. In particular, Rosenbrock method which is able to do direct search without obtaining differential coefficient was adopted for the back analysis algorithm among optimization methods. This back analysis program was applied to the site to evaluate the design parameters. The back analysis was carried out using field measurement results from 5 sites. In the course of back analysis, nonlinear regression analysis was carried out to identify the optimum function of the measured ground displacement. Exponential function and fractional function were used for the regression analysis and total displacement calculated by optimum function was used as the back analysis input data. As a result, displacement recalculated through the back analysis using measured displacement of the structure showed 4.5% of error factor comparing to the measured data. Hence, the program developed in this study proved to be effectively applicable to tunnel analysis.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.6
• /
• pp.34-39
• /
• 2001

An adaptive multi-rate (AMR) speech coder was adopted as a standard of W-CDMA by 3GPP and ETSI. The AMR coder is based on the CELP algorithm operating at rates ranging from 12.2 kbps down to 4.75 kbps, and it is a source controlled codec according to the channel error conditions and the traffic loading. In this paper, we implement the DSP S/W of the AMR coder using OakDSPCore. The implementation is based on the CSD17C00A chip developed by C&S Technology, and it is tested using test vectors, for the AMR speech codec, provided by ETSI for the bit exact implementation. The DSP B/W requires 20.6 MIPS for the encoder and 2.7 MIPS for the decoder. Memories required by the Am coder were 21.97 kwords, 6.64 kwords and 15.1 kwords for code, data sections and data ROM, respectively. Also, actual sound input/output test using microphone and speaker demonstrates its proper real-time operation without distortions or delays.

• Environmental and Resource Economics Review
• /
• v.24 no.3
• /
• pp.523-549
• /
• 2015

This paper investigates an oil refiner's asymmetric behavior in the adjustments of gasoline and diesel prices to changes in his own price and his rivals' prices as well as input costs. An asymmetric error correction model which allows a firm's pricing behavior to the deviation of other firms' prices from their long-run equilibrium level is employed for estimation using weekly data for the period April 2009 to January 2015. Evidence is found that there is a significant degree of asymmetry in the adjustment of wholesale prices to changes in crude oil price. A similar result in regard to the exchange rate is also found by the data. The estimation results for firm's response to changes in other firms' prices indicates that implicit collusion could be more easily exploited in the wholesale petroleum market as results of firms' interaction with each other and anticipation of rivals' pricing behavior. A few refiners show competitive price adjustment in response to the upward deviation of the others' prices from their equilibrium level.

• Korean Journal of Remote Sensing
• /
• v.32 no.6
• /
• pp.567-577
• /
• 2016

SqueeSAR is a new technique to combine Persistent Scatterer (PS) and Distributed Scatterer (DS) for deformation monitoring. Although many PSs are available in urban areas, SqueeSAR analysis can be beneficial to increase the PS density in not only natural targets but also smooth surfaces in urban environment. The height of each targets is generally required to remove topographic phase in interferometric SAR processing. The result of PSInSAR analysis to use PS only is not affected by DEM resolution because the height error of initial input DEM at each PSs is precisely compensated in PS processing chain. On the contrary, SqueeSAR can be affected by DEM resolution and precision since it includes spatial average filtering for DS targets to increase a signal-to-noise ratio (SNR). In this study we observe the effect of DEM resolution on deformation measurement by PSInSAR and SqueeSAR. With ALOS-1 PALSAR L-band data, acquired over Daejeon city, Korea, two different DEM data are used in InSAR processing for comparison: 1 m LIDAR DEM and SRTM 1-arc (~30 m) DEM. As expected the results of PSInSAR analysis show almost same results independently of the kind of DEM, while the results of SqueeSAR analysis show the improvement in quality of the time-series in case of 1-m LIDAR DSM. The density of InSAR measurement points was also improved about five times more than the PSInSAR analysis.