• Clean Technology
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• v.29 no.2
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• pp.145-150
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• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.79-86
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• 2021

In order to increase the probability of success in big data projects, quantified techniques are required to analyze the root cause of risks from complex causes and establish optimal countermeasures. To this end, this study measures risk factors and relationships through SNA analysis and presents a way to respond to risks based on them. In other words, it derives a dependency network matrix by utilizing the results of correlation analysis between risk groups in the big data projects presented in the preliminary study and performs SNA analysis. In order to derive the dependency network matrix, partial correlation is obtained from the correlation between the risk nodes, and activity dependencies are derived by node by calculating the correlation influence and correlation dependency, thereby producing the causal relationship between the risk nodes and the degree of influence between all nodes in correlation. Recognizing the root cause of risks from networks between risk factors derived through SNA between risk factors enables more optimized and efficient risk management. This study is the first to apply SNA analysis techniques in relation to risk management response, and the results of this study are significant in that it not only optimizes the sequence of risk management for major risks in relation to risk management in IT projects but also presents a new risk analysis technique for risk control.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.924-935
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• 2023

Purpose: The central aim of this study is to leverage machine learning techniques for the classification of Intrusion Detection System (IDS) data, with a specific focus on identifying the variables responsible for enhancing overall performance. Method: First, we classified 'R2L(Remote to Local)' and 'U2R (User to Root)' attacks in the NSL-KDD dataset, which are difficult to detect due to class imbalance, using seven machine learning models, including Logistic Regression (LR) and K-Nearest Neighbor (KNN). Next, we use the SHapley Additive exPlanation (SHAP) for two classification models that showed high performance, Random Forest (RF) and Light Gradient-Boosting Machine (LGBM), to check the importance of variables that affect classification for each model. Result: In the case of RF, the 'service' variable and in the case of LGBM, the 'dst_host_srv_count' variable were confirmed to be the most important variables. These pivotal variables serve as key factors capable of enhancing performance in the context of classification for each respective model. Conclusion: In conclusion, this paper successfully identifies the optimal models, RF and LGBM, for classifying 'R2L' and 'U2R' attacks, while elucidating the crucial variables associated with each selected model.

• Ocean and Polar Research
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• v.36 no.4
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• pp.327-341
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• 2014

This study aims to analyze causality among Wando abalone producer prices by size using a vector autoregressive model to expiscate the leading-price of Wando abalone in various price classes by size per kg. This study, using an analytical approach, applies a unit-root test for stability of data, a Granger causality test to learn about interaction among price classes by size for Wando abalone, and a vector autoregressive model to estimate the statistical impact among t-1 variables used in the model. As a result of our leading-price analysis of Wando abalone producer prices by shell size using a VAR model, first, DF, PP, and KPSS tests showed that the Wando abalone monthly price change rate by size differentiated by logarithm were stable. Second, the Granger causality relationship analysis showed that the price change rate for big size abalone weakly led the price change rate for the small and medium sizes of abalone. Third, the vector autoregressive model showed that three price change rates of t-1 period variables statistically, significantly impacted price change rates of own size and other sizes in t period. Fourth, the impulse response analysis indicated that the impulse responses of structural shocks for price change rate for big size abalone was relatively more powerful in its own size and in other sizes than shocks emanating from other sizes. Fifth, the variance decomposition analysis indicated that the price change rate for big size abalone was relatively more influential than the price change rates for medium and small size abalone.

• Korean Journal of Medicinal Crop Science
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• v.10 no.5
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• pp.317-326
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• 2002

This study was conducted to determine the optimum planting dates and density of one year old Bupleurum falcatum L. to improve its productivity and quality. Two cultivars of B. falcatum, originated from Jeongseon, Korea and Mishima, Japan were used. Some of the results obtained are as follows : Jeongseon cultivar showed less stem branches and shoot weight compared to Mishima. However, Jeongseon cultivar showed tall plant height, high root fresh and dry weight, and high levels of saikosaponin, but low saikosaponin content than that of Mishima. Both cultivars seeded on March 20 had long main root, big stem diameter, few stem branch, and high saikosaponin c content compared to those of late seeded one, April 30. Growth characteristics such as plant height, stem diameter, stem branch number, shoot weight, root diameter, root fresh and dry weight, and root branch number were increased in a low planting $density(30\;{\times}\;15cm)$, but the content of saikosaponin was not affected by planting density. Jeongseon and Mishima cultivars seeded on April 10 with $30\;{\times}\;15cm$ planting density and April 30 with $30\;{\times}\;10cm$ planting density contained the highest total saikosaponin levels, respectively. However, average root dry weight were not affected by planting time or density in both Bupleurum cultivars.

• The korean journal of orthodontics
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• v.21 no.3
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• pp.603-614
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• 1991

To study the possibility of attraction magnetic forces to expand maxillary arch, we used 2 big adult dogs, 2 small puppies, 1 small adult dog as experiments, and 1 small adult dog as a control. We measured the intercanine width and intermolar width and histologically observed in the suture and cervical and apex region of teeth and took occlusal X-rays to observe separation of suture line in the maxilla. The results were as follows: 1. Expansion velocities of intercanine (0.25mm/day) and intermolar widths (0.23mm/day) in puppies were faster than those (0.135mm/day, 0.09mm/day) in adults. 2. In all experiments in adults (0.135mm/day) and puppies (0.25mm/day), expansion velocity of intercanine widths were faster than those (0.09mm/day, 0.23mm/day) of intermolar width. 3. In all experiments ectatic changes were observed and cellularities of fibroblast increased in the suture line. Only in adults dogs the separations of palatal suture were observed in the occlusal X-ray view. 4. In the puppies bony deposition was particularly observed in the suture line and micro-bony fragments were often observed. 5. In the all experiments no root resorption was observed in the cervical and root area, but normal root resorption due to eruption of permanent teeth was observed in the puppies.

• Korean Journal of Medicinal Crop Science
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• v.19 no.4
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• pp.227-232
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• 2011

The roots of Platycodon grandiflorum has been widely used as a crude drug or a food stuff. Unfortunately, the output and the quality is not regular and highly dependent on the cultivation area and cultivation method. Therefore, seedling cultivation study of this plant under structure with various fertilizer supply was performed. As a result, significant big difference between ammonia nitrogen and nitrate nitrogen content was shown in the root at seedling stage while the difference was not significant in the aerial parts. Fresh weight of the root (7.73 g plant$^{-1}$) was higher in the group treated with three major nutrients (N, P and K) than in those treated with three major nutrients and calcium or magnesium or both calcium and magnesium, and non-treated group (2.69 g plant$^{-1}$). Total nitrogen content was recognized to be significantly correlated with root weight, plant height, number of leaves and weight of aerial parts. Ammonium nitrogen content was more correlated the growth of P. grandiflorum than nitrate nitrogen. For phosphoric acid, significant correlation was also shown with the four growth factors.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Korean Journal of Medicinal Crop Science
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• v.2 no.2
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• pp.114-120
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• 1994

Experiment was conducted to investigate the effects of planting date and planting methods with seedling size on bolting and yield of Ostericum koreanum (MAX.) KITAGAWA Seeding methods were also reviewed to imvestigate their effects on seedling characteristics. This experiment was carried out in Chuncheon during 1988 growing season. The small seeding rate and broadcasting had higher rates of emergence than the others. There was no big difference in seedling size by seedling rate, and more seedling growth was in drilling methods among seed planting methods. The quantity of seedling were produced with the order of medium, small and lastly large seedling. Dense planting $(8l\;/33m^2)$ was advantageous in producing small seedling. Large seedling had earlier flowering than the others and growth was good in planting small seedling with drilling method. The rates of bolting by seedling sizes were 89.6% in large, 64.6% in medium and 36.9% in small seedling. Bolting was influenced the root Quality by producing lignified root which had a least commercial value. More root growth was shown in unbolting plant compared to bolting plant seedling and broadcasting had much more root growth than seedling from drilling. Fresh root yield of unbolting plant was higher than that of bolting plant and highest yield was obtained in the broadcasting plot with small seedling.