• Journal of Korea Foresty Energy
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• v.25 no.2
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• pp.1-8
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• 2006

The physical, mechanical and chemical properties of old and new Korean red pine (Pinus densiflora) were analyzed. The old woods were from dismantled timbers of Bonjungsa temple. The crystallized resin in the latewood was observed by microscopic analysis. Also, reduction of specific gravity, occurrence of microscopic cleavage of tracheid was observed in the old wood. The angle of microscopic cleavage of tracheid is estimated with the same angle of micro-fibril angle of 52 layer. The bending, compression and shear strength of old world were decreased about 35-27% than those of new wood. Dynamic modulus of elasticity measured by ultrasonic nondestructive test has the tendency of reducing by the time elapse of the wood usage. Therefore, deterioration of wood could be measured by reduction of specific gravity and dynamic MOE. The static MOE and mechanical properties of old wood could be predictable by measuring dynamic MOE in the longitudinal direction. Extractives of the old wood in 1-% NaOH solution are larger quantity than new wood. Therefore the decay of the wood could be evaluated by analyzing the chemical compound, especially 1-% NaOH solution. The results of this research could be used for understanding and prediction of the changing properties with elapsing time of wood.

• Journal of Bio-Environment Control
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• v.23 no.2
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• pp.123-130
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• 2014

Oriental melon (Cucumis melo var. makuwa) should be cultivated on the soil and be harvested. It is difficult to find because it is covered with leaves, and furthermore, it is very hard to grip it due to its climbing stems. This study developed and tested oriental melon harvesting robots such as an end-effector, manipulator and identification device. The end effector is divided into a gripper for harvest and a cutter for stems. In addition, it was designed to control the gripping and cutting forces so that the gripper could move four fingers at the same time and the cutter could move back and forth. The manipulator was designed to realize a 4-axis manipulator structure to combine orthogonal coordinate-type and shuttle-type manipulators with L-R type model to rotate based on the central axis. With regard to the identification device, oriental melon was identified using the primary identification global view camera device and secondary identification local view camera device and selected in the prediction of the sugar content or maturity. As a result of the performance test using this device, the average harvest time was 18.2 sec/ea, average pick-up rate was 91.4%, average damage rate was 8.2% and average sorting rate was 72.6%.

• Journal of Soil and Groundwater Environment
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• v.8 no.2
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• pp.55-61
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• 2003

The objective of this study was to investigate the characteristics of nitrate removal by conducting the column test in order to see the performance of surfactant modified zeolite (SMZ) as a permeable reactive barrier material. The prediction of nitrate removal was tested using the one-dimensional advective-dispersive model fitted to the experimental breakthrough curve. A methodology for scaling up to in-situ permeable reactive barrier was also proposed. The breakthrough of nitrate in the column packed with SMZ was well predicted using linear equilibrium adsorption model. The breakthrough time and half-life obtained by breakthrough experiment with variation of flowrates were decreased with the increase of flowrates. When 10㎥/day of groundwater containing the 50 mg/l of nitrate is to be treated to satisfy the potable water quality criteria (10 mg/l) by SMZ reactive barrier, 300 tons of SMZ and about 6 years of breakthrough time will be required, suggesting that 165 million wons are needed as barrier material expenses in each 6 years besides the initial design and construction expenses and the minimal monitoring and maintenance expenses.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.189-200
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• 2023

As urbanization and industrialization increase development in downtown areas, damage due to ground settlement continues to occur. Building collapse in urban has a high risk of leading to large-scale damage to life and property. However, there has rarely been studied on measurement data analysis methods when uneven loads are applied to the excavated ground and no prior knowledge of the ground. Accordingly, it was attempted to analyze the relative settlement behavior and correlation by processing the time-series surface settlement of construction sites in the urban. In this paper, the average index of difference in settlement and average of relative difference in settlement are defined and calculated, then plotted in the coordinate system to analyze the relative settlement behavior over time. In addition, since there was no prior knowledge of the ground, a standard to classify the clusters was needed, and the observation points were classified into using k-means clustering and Dunn Index. As a result of the analysis, it was confirmed that all the clusters moved to the stable region as the settlement amount converges. The clusters were segmented. Based on the analysis results, it was possible to distinguish between the independent displacement area and same behavior area by analyzing the correlation between measurement points. If possible to analyze the relative settlement behavior between the stations and classify the behavior areas, it can be helpful in settlement and stability management, such as uplift of the surrounding area, prediction of ground failure area, and prevention of activity failure.

• Korean Circulation Journal
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• v.53 no.3
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• pp.170-184
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• 2023

Background and Objectives: Several cases involving severe right ventricular (RV) failure in advanced cancer patients have been found to be pulmonary tumor thrombotic microangiopathies (PTTMs). This study aimed to discover the nature of rapid RV failure syndrome with a suspected diagnosis of PTTM for better diagnosis, treatment, and prognosis prediction in clinical practice. Methods: From 2011 to 2021, all patients with clinically suspected PTTM were derived from the one tertiary cancer hospital with more than 2000 in-hospital bed. Results: A total of 28 cases of clinically suspected PTTM with one biopsy confirmed case were included. The most common cancer types were breast (9/28, 32%) and the most common tissue type was adenocarcinoma (22/26, 85%). The time interval from dyspnea New York Heart Association (NYHA) Grade 2, 3, 4 to death, thrombocytopenia to death, desaturation to death, admission to death, RV failure to death, cardiogenic shock to death were 33.5 days, 14.5 days, 7.4 days, 6.4 days, 6.1 days, 6.0 days, 3.8 days and 1.2 days, respectively. The NYHA Grade 4 to death time was 7 days longer in those who received chemotherapy (7.1 days vs. 13.8 days, p value=0.030). However, anticoagulation, vasopressors or intensive care could not change clinical course. Conclusions: Rapid RV failure syndrome with a suspected diagnosis of PTTM showed a rapid progressive course from symptom onset to death. Although chemotherapy was effective, increased life survival was negligible, and treatments other than chemotherapy did not help to improve the patient's prognosis.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.158-169
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• 2019

A probabilistic model that can predict the residual useful lifetime of structure is formulated by using the gamma process which is one of the stochastic processes. The formulated stochastic model can take into account both the sampling uncertainty associated with damages measured up to now and the temporal uncertainty of cumulative damage over time. A method estimating several parameters of stochastic model is additionally proposed by introducing of the least square method and the method of moments, so that the age of a structure, the operational environment, and the evolution of damage with time can be considered. Some features related to the residual useful lifetime are firstly investigated into through the sensitivity analysis on parameters under a simple setting of single damage data measured at the current age. The stochastic model are then applied to the rubble-mound breakwater straightforwardly. The parameters of gamma process can be estimated for several experimental data on the damage processes of armor rocks of rubble-mound breakwater. The expected damage levels over time, which are numerically simulated with the estimated parameters, are in very good agreement with those from the flume testing. It has been found from various numerical calculations that the probabilities exceeding the failure limit are converged to the constraint that the model must be satisfied after lasting for a long time from now. Meanwhile, the expected residual useful lifetimes evaluated from the failure probabilities are seen to be different with respect to the behavior of damage history. As the coefficient of variation of cumulative damage is becoming large, in particular, it has been shown that the expected residual useful lifetimes have significant discrepancies from those of the deterministic regression model. This is mainly due to the effect of sampling and temporal uncertainties associated with damage, by which the first time to failure tends to be widely distributed. Therefore, the stochastic model presented in this paper for predicting the residual useful lifetime of structure can properly implement the probabilistic assessment on current damage state of structure as well as take account of the temporal uncertainty of future cumulative damage.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.117-122
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• 2008

Quality control of the concrete pavement in the early stage of curing is very important because it has a conclusive effect on its life span. Therefore, examining and analyzing the initial behavior of concrete pavement must precede an alternative to control its initial behavior. There are largely two influential factors for the initial behavior of concrete pavement. One is the drying shrinkage, and the other is the heat generated by hydration and thermal change inside the pavement depending on the change in the atmospheric temperature. Thus, the coefficient of thermal expansion and drying shrinkage can be regarded as very important influential factors for the initial behavior of the concrete. It has been a general practice up until now to measure the coefficient of thermal expansion from completely cured concrete. This practice has an inherent limitation in that it does not give us the coefficient of thermal expansion at the initial stage of curing. Additionally, it has been difficult to obtain the measurement of drying shrinkage due to the time constraint. This research examined and analyzed the early drying shrinkage of the concrete and measurements of the thermal expansion coefficients to formulate a plan to control its initial behavior. Additionally, data values for the variables of influence were collected to develop a prediction model for the initial behavior of the concrete pavement and the verification of the proposed model. In this research, thermal expansion coefficients of the concrete in the initial stage of curing ranged between $8.9{\sim}10.8{\times}10^{-6}/^{\circ}C$ Furthermore, the effects of the size and depth of the concrete on the drying shrinkage were analyzed and confirmed.