• Journal of Environmental Science International
• /
• v.33 no.7
• /
• pp.453-462
• /
• 2024

This study was carried out for the purpose of selecting the most appropriate taper equation for the actual stands of Pinus thunbergii in the southern coastal region of Korea and then developing a stem volume table to provide basic data for rational management. To develop a volume table of Pinus thunbergii in this region of Korea, 59 sample trees with various diameter distributions were selected and stem analysis was performed. As a result of stem analysis, two trees with abnormal diameter and height growth as the age increased were rejected, and 57 trees were analyzed. To develop the taper equation, seven major variable exponential equations were used, including Kozak 1988, 1994, 2001, 2002, Bi 2000, Muhairwe 1999, and Sharma and Parton 2009. As a result of parameter estimation and statistical verification, the Kozak 1988 model showed the highest goodness of fit with Fit I (Fit Index), RMSE 1.5620, Bias 0.0031, and MAD 1.0784. The diameter of each 10cm stem ridge for the selected model was estimated, and a stem volume table was produced using the mensuration of division (end area formula) using the Smalian equation. As a result of two-sample T-test for volume table of this study and current yield table, the volume for this study was found to be significantly larger at all observation points (p < 0.001). Even for the same tree species, it is judged that differentiated volume tables are needed for each growth environment characteristic.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.27 no.4
• /
• pp.1-14
• /
• 2024

Otters, designated as Class I endangered wildlife due to population declines resulting from urban development and stream burial, have seen increased appearances in freshwater environments since the nationwide ban on stream filling in 2020 and the implementation of urban stream restoration projects. There is a pressing need for scientific and strategic conservation measures for otters, an umbrella and vulnerable species in aquatic ecosystems. Therefore, this study predicts potential otter habitats using the species distribution model MaxEnt, focusing on Hwangguji Stream in Suwon, and proposes conservation strategies. Otter signs were surveyed over three years from 2019 to 2021 with citizen scientists, serving as presence data for the model. The model's outcomes were enhanced by analyzing 'river nature map' as a boundary. MaxEnt compared the performance of 60 combinations of feature classes and regularization multipliers to prevent model complexity and overfitting. Additionally, unmanned sensor cameras observed otter density for model validation, confirming correlations with the species distribution model results. The 'LQ-5.0' parameter combination showed the highest explanatory power with an AUC of 0.853. The model indicated that the 'adjacent land use' variable accounted for 31.5% of the explanation, with a preference for areas around cultivated lands. Otters were found to prefer shelter rates of 10-30% in riparian forests within 2 km of bridges. Higher otter densities observed by unmanned sensors correlated with increasing model values. Based on these results, the study suggests three conservation strategies: establishing stable buffer zones to enhance ecological connectivity, improving water quality against non-point source pollution, and raising public awareness. The study provides a scientific basis for potential otter habitat management, effective conservation through governance linking local governments, sustainable biodiversity goals, and civil organizations.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.139-157
• /
• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Investigative Magnetic Resonance Imaging
• /
• v.5 no.1
• /
• pp.33-37
• /
• 2001

Purpose : The water exchange rate between bulk water and bound water is an important parameter in deciding the efficiency of paramagnetic contrast agents. In this study, we evaluated the water exchange rates of various Gd-chelates using oxygen-17 NMR technique. Material and Methods : The samples (Gd-DTPA, Gd-DTPA-BMA, Gd-DOTA, Gd-EOB-DTPA) were prepared by mixing 5% $^{17}O-enriched$ water (Isotech, USA). The pH of the samples was adjusted to physiological value [pH=7.0] by buffer solution. The variable temperature $^{17}O-NMR$ measurements were performed using Bruker-600 (14.1 T, 81.3 MHz) spectrometer. Bruker VT-1000 temperature control units were used to stabilize the temperature. The $^{17}O$ spin-spin relaxation times (T2) were measured using Carr-Purcell-Meiboom-Gill (CPMG)I pulse sequence with 24 echo trains. The variable temperature T2 relaxation data were then fitted into Solomon-Bloembergen equations using least square fit algorithm to estimate the water exchange times. Results : From the measured $^{17}O-NMR$ relaxation rates, the determined water exchange rates at 300K are $0.42{\;}{\mu}s$ for Gd-DTPA, $1.99{\;}{\mu}s$ for Gd-DTPA-BMA, $0.27{\;}{\mu}s$ for Gd-DOTA, and $0.11{\;}{\mu}s$ for Gd-EOB-DTPA. The Gd-DTPA-BMA showed slowest exchange whereas Gd-EOB-DTPA had fastest water exchange rate. In addition, it was found that the water exchange rates (${\tau}_m$) of all samples had exponential temperature dependence with different decay constant. Conclusion : $^{17}O-NMR$ relaxation rate measurements, when combined with variable temperature technique, provide a solid tool for studying water exchange rate, which is very important in investigating the detailed mechanism of relaxation enhancement effect of the paramagnetic contrast agents.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.40 no.3
• /
• pp.273-283
• /
• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Venture Innovation
• /
• v.3 no.2
• /
• pp.41-76
• /
• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.18 no.12
• /
• pp.1870-1883
• /
• 1993

In this paper, in order to build up the User Network Interface based on ATM, a study on traffic control techniques which should be performed by main function groups-B 75,5 NT2, LEX-is discussed. The structure of B-NT2 which is the most important function group In the User Network Interface is defined in quite a simple manner in addition, the functional blocks of LEX are defined in a similar manner as those of B NT2. It is possible to distribute total traffic control functions by using the similarities between B-NT2 and LEX and by allocating virtual path identifiers fixedly according to the characteristics of the traffics. For the traffic control techniques of ATM, relations among Connection Admtsslon Control, Usage Parameter Control and Bandwidth Allocation Control are defined and Multi Rule Base structure to realize optimal control functions according to the characteristics of the source traffics is proposed. And the Real-time Variable Window algorithmsimply designed to be suitable for the Multi Rule Base architecture is also proposed. The performances of the proposed algorithm are analyzed through the computer simulation by generating on-off source traffic in a virtual system that has the form of the proposed hardware. The analyzed results show that the distributed control is possible and that the implementation of the proposed architecture and algorithm is possible.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.9 no.5 s.45
• /
• pp.41-52
• /
• 2005

Using the variable control gain scheme on the basis of fuzzy-based decision-making process, Fuzzy supervisory control (FSC) technique exhibits better control performance than linear control technique with one static control gain. This paper demonstrates the effectiveness of the FSC technique by evaluating the robust performance of the FSC technique under the presence of uncertainties in the models and the excitations. Robust performance of the FSC system is compared with that of optimally designed LQG control system for the benchmark cable-stayed bridge presented by Dyke et al. Parameter studies on the robust performance evaluation are carried out by varying the stiffness of the bridge model as well as the magnitudes of several earthquakes with different frequency contents. From the comparative study of two control systems, FSC system shows the enhanced control performance against various magnitudes of several earthquakes while maintaining lower level of power required for controlling the bridge response. Especially, FSC system clearly guarantees the improved robust performance of the control system with stable reduction effects on the seismic responses and slight increases in total power and stroke for the control system, while LQG control system exhibits poor robust performance.

• Journal of the Korea Concrete Institute
• /
• v.21 no.3
• /
• pp.283-290
• /
• 2009

Differently from fly ash, the bottom ash produced from thermoelectric power plant has been treated as an industrial waste matter, and almost reclaimed a tract from the sea. If this waste material is applicable to foam concrete as an aggregate owing to its light-weight, however, it may be worthy of environmental preservation by recycling of waste material as well as reducing self-weight of high-rising structure and horizontal forces and deformations of retaining wall subject to soil pressure. This study has an objective of evaluating the effects of application of bottom ash on the mechanical properties of foam concrete. Thus, the ratio of bottom ash to cement was selected as a variable for experiment and the effect was measured in terms of unit weight of concrete, air content, water-cement ratio and compressive strength. It can be observed from experiments that the application ratios have different effects on the material parameters considered in this experiment, thus major relationships between application ratio and each material parameter were finally introduced. The result of this study can be applied to decide a mix design proportion of foam concrete while bottom ash is used as an aggregate of the concrete.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.9C
• /
• pp.849-860
• /
• 2007

Because the H.264/AVC standard adopts the variable length coding algorithm, the rate of encoded video bitstream fluctuates a lot as time flows, though its compression efficiency is superior to that of existing standards. When a video is transmitted in real-time over networks with fixed low-bandwidth, it is necessary to control the bit rate which is generated from encoder. Many existing rate control algorithms have been adopting the quadratic rate-distortion model which determines the target bits for each frame. We propose a new rate control algorithm for H.264/AVC video transmission over networks with fixed bandwidth. The proposed algorithm predicts quantization parameter adaptively to reduce video distortion using the quadratic rate-distortion model, which uses the target bit rate and the mean absolute difference for current frame considering pixel difference between macroblocks in the previous and the current frame. On video samples with high motion and scene change cases, experimental results show that (1) the proposed algorithm adapts the encoded bitstream to limited channel capacity, while existing algorithms abruptly excess the limit bit rate; (2) the proposed algorithm improves picture quality with $0.4{\sim}0.9dB$ in average.