• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.1
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• pp.9-17
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• 2009

Twenty years have passed since the adoption of natural recreation forests and each forest has its own characteristics. However, there is hardly any classification among the natural recreation forests. The purpose of this study is to classify the forests by considering the supplier's perspective as well as the user's perspective in order to provide fundamental materials for the operation of the natural recreation forests. A factor analysis was conducted to identify the common characteristics of the selected twelve variables by pre-selection and survey of experts. K-means cluster analysis was conducted among those factors to classify the natural recreation forests in Korea. Four factors were drawn after the factor analysis and the factors were named according to the variables and sizes as 'The use performance and visiting condition factor', 'Education and settlement factor', 'Internal activation factor' and 'Potential factor' In addition, the cluster analysis of an $85{\times}4$ matrix was conducted for the points of the drawn factors and the final classification consists of five groups. The results of this study may contribute to providing fundamental materials for the operation and management of natural recreation forests. Also, it may act as a reference when investigating the natural recreation forests of Korea. Proposing the classification natural recreation forests could be helpful in selecting the proper recreation forest in the future. Based on the established model, fundamental materials could be provided to improve the profitability of the natural recreation forests by effectively expanding the number of tourists, creating new natural recreation forests and proper maintenance and management.

• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.390-397
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• 2013

The study, utilizing a data envelopment analysis (DEA) which is one of the nonparametric estimation methods, aims to evaluate the management efficiency of chestnut tree cultivators in such provinces in Chungchungnam-do as Cheong-yang, Gong-ju, Bu-yeo and so on. The analysis data of this study is based on inputs and outputs of 20 forestry households surveyed in the 2012 survey titled 'A Study on Current Level and Condition of Chestnut Cultivation and Management', which was conducted from March 2012 to October 2012. The elements of inputs are composed of management cost, harvesting cost, material cost, non-operation expenses and cultivation area, while the element of output is a gross margin only. Then the study analyzes a technical efficiency, a puretechnical efficiency and a scale efficiency using CCR and BCC model among DEA methods. Based on that, it also provides improvement methods for forestry households that turned out to be inefficient. In order to verify the result of DEA analysis, the study additionally compares a result of this efficiency study with that of chestnuts management standard diagnostic table. According to the result, the average value of technical efficiency analyzed was 0.667, proving to be inefficient in general. Given that the average value of pure-technical efficiency was 0.944 and that of scale efficiency was 0.703, it can be inferred that inefficiency exists in the field of scale, not in the field of cultivation techniques. As for forestry households with the efficiency score of 1, it is shown that there were 6 households that recorded 1 in the technical efficiency field and 13 households that recorded 1 in the pure technical efficiency. Meanwhile, there were 6 households that recorded 1 in all of the three aspects. In the comparison with the scores from chestnuts management standard diagnostic table, there were 5 households made a high score of over 80, among which are 3 households with score 1 in the technical efficiency. Also, the results of this study and the chestnuts management standard diagnostic table are proved to have the same result, both of them showing the same households that recorded the highest score and the lowest score. This means the management efficiency evaluation using DEA can be applied to the fieldwork along with the chestnuts management standard diagnostic table.

• The Korean Journal of Mycology
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• v.31 no.2
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• pp.89-93
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• 2003

In order to select suitable bioreactor type inhibiting cell stress during submerged culture of Tricholoma matsutake mycelium, the growth characteristics and ergosterol contents were investigated using the external-loop type of air-lift bioreactor (ETAB), balloon type of air bubble bioreactor (BTBB) and column type of air bubble bioreactor (CTBB). Dry weights of the T. matsutake in the BTBB, ETAB and CTBB were 12 g, 11.4 g, and 9.5 g per 1 litter, respectively. BTBB, ETAB and CTBB reached stagnant phases 16, 20, and 24 days after cultivation, respectively, The BTBB was more suitable for liquid culture of T. matsutake mycelium compared to other bioreactors owing to much mycelia product and short culture period. The ergosterol contents produced by the mycelium in the bioreactors were in sequence of BTBB, CTBB, and ETAB at every growth phase. BTBB might affect the mycelium on producing the smallest size of pellets. BTBB and CTBB got the mycelium precipitated and coagulated under operation of bioreactor sparser, whereas ETAB shown no effect of above phenomenon. A renovated bioreactor combined between a balloon shape of BTBB and an external-loop of ETAB was developed to enhance the efficiency of culture technique.

• Journal of Wetlands Research
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• v.23 no.1
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• pp.27-34
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• 2021

In this study, the main influencing factors of the occurrence of cyanobacteria at each of the eight Multifunctional weirs were derived using a random forest, and a categorical prediction model based on a Algal bloom warning system was developed. As a result of examining the importance of variables in the random forest, it was found that the upstream points were directly affected by weir operation during the occurrence of cyanobacteria. This means that cyanobacteria can be managed through efficient security management. DO and E.C were indicated as major influencers in midstream. The midstream section is a section where large-scale industrial complexes such as Gumi and Gimcheon are concentrated as well as the emissions of basic environmental facilities have a great influence. During the period of heatwave and drought, E.C increases along with the discharge of environmental facilities discharged from the basin, which promotes the outbreak of cyanobacteria. Those monitoring sites located in the middle and lower streams are areas that are most affected by heat waves and droughts, and therefore require preemptive management in preparation for the outbreak of cyanobacteria caused by drought in summer. Through this study, the characteristics of cyanobacteria at each point were analyzed. It can provide basic data for policy decision-making for customized cyanobacteria management.

• Atmosphere
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• v.20 no.2
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• pp.187-194
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• 2010

We estimate the economic benefit of weather modification (precipitation enhancement and fog dissipation) by assuming its operation for the considered regions. Based on the statistical data, the economic benefit of the virtually operational precipitation enhancement experiments for the Andong and Imha basins, where the natural precipitation is relatively lack in South Korea, is calculated 348 for the water resources, 22,458 for forest fire prevention, and 28,458 million won/year for the drought relief. The benefit of the fog dissipation operation for the Incheon International Airport is estimated 7,365 million won/year for the flight delay due to fog. The calculated ratio of benefit to cost for precipitation enhancement operation for the basins is 14.07, which is comparable to that conducted in other countries.

• Journal of People, Plants, and Environment
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• v.22 no.1
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• pp.1-14
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• 2019

This study was designed to examine agro-healing services and programs provided and operated by farms in Korea. The results of the analysis of the agro-healing programs and operation of farms were as follows. The purpose of the operation of farms was to raise productivity by managing farms in a cooperative way through agricultural production, education and healing, and to raise income by processing and selling agricultural products. It was difficult to access farms by public transport and thus visitors had to use their own cars. The size of farms varied. The main resources utilized in the surveyed programs were plants, rural environments and landscapes, and agricultural products. The programs were conducted using resources that were commonly found in rural areas. Facilities on each farm were equipped with facilities (indoor and outdoor learning place, gardens, vegetable gardens, orchards, etc.) and convenience facilities (parking lots, drinking fountains, kiosks, etc.) to support program operation. However, facilities for the handicapped and accommodation facilities were insufficient. The programs operated on each farm utilized agricultural resources, farm produce, and rural resources and were classified into activities such as making, feeling, and growing. The average number of people who operated the family-centered program was 2-3, having qualifications such as welfare horticultural therapists, forest interpreters, experience instructors, and social workers. In addition, they had expertise in medicinal food, dietary life, and social welfare, and they also had essential expertise required to operate programs.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.289-294
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• 1997

The chestnut is a well-known and important forest product in Korea. The annual production of chestnut is about 100, 000tons and its cultivating area is 80, 000ha. However, the peeling process of outer and inner skins of chestnut is very difficult due to hardness and adhesiveness of chestnut skin. The peeling process of chestnut was operated by manual work and the performance of chestnut peeling machine is very low. The purpose of this study is to develope the prototype of new chestnut peeling system. The hardness of chestnuts was tested with six different drying conditions and its range was from 949$g/mm^2$ to 2, 149$g/mm^2$. The hardness of chestnuts was decresed gradually during the drying process. The chestnut peeling Process includes sorting, storage, drying, outer skin cutting, flame peeling, continuous frictional skin peeling, and inner skin cutting operation. The developed chestnut peeling system consists of outer skin cutter, flame peeler, continuous frictional skin peeler and inner skin cutter. The system can peel domestic chestnuts at 150$kg/hr$ with peeling rate of 78%.

• The Plant Pathology Journal
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• v.21 no.1
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• pp.1-6
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• 2005

During last decade there has been noticeable progress in the research of the biology of sapstaining fungi that cause considerable economic losses to forest product industry. The researches generated broad ranges of knowledge on sapstaining fungi regarding their occurrence on conifer wood, taxonomy, nutrient physiology, pigmentation biochemistry and molecular biology, and biological control. Major problematic groups in the sapstain production are Ophiostoma, Ceratocystis, and Leptographium genera. With Ophiostoma as a model, it is found that the type of carbon source is important in the growth and pigment production of sapstaining fungi. The operation of dihydroxy naphthalene (DHN) melanin pathway for black to bluish pigment production has been confirmed in those cosmetic fungi both at biochemical and molecular levels. The development of albino technology using nutrition competition has been shown to be promising as an environmentally friendly biological control method for sapstain control.