• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.135-139
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• 2007

Recently Data Centric Storage (DCS) schemes are variously studied for several applications (e.g. natural environment investigation, military application systems and environmental changes monitoring). In DCS scheme, data is stored at nodes within the network by name. There are several drawbacks in the existing schemes. The first is the inefficiency of the range query processing on not considered the locality of store point. the second is the non-homogeneity of store load of each sensors in case of the sensor distribution density is non-uniformed. In this paper, we propose a novel data centric storage scheme with the sensor distribution density which satisfied with the locality of data store location. This scheme divides whole sensor network area using grid and distributes the density bit map witch consist of the sensor density information of each cell. sensors use the density bit map for storing and searching the data. We evaluate our scheme with existing schemes. As a result, we show improved load balancing and more efficient range query processing than existing schemes in environment which sensors are distributed non-uniform.

• The Korean Journal of Pesticide Science
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• v.17 no.2
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• pp.107-116
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• 2013

Polar pesticides like pymetrozine (log $P_{ow}$: -0.18) are known to be difficult to analyze. The analytical method of pymetrozine using hydromatrix included in the official method of KFDA was uncommon and provided ambiguous evidence to confirm both the identity and the quantity. Therefore, precise single residue analytical method was developed in representative crops for using liquid-liquid extraction (LLE). The pymetrozine residue was extracted with methanol from 11 representative crops which comprised apple, blueberry, broccoli, cabbage, cherry, crown daisy, hulled rice, Korean cabbage, potato, rice and watermelon. The extract was purified serially by liquid-liquid extraction (LLE) and silica solid phase extraction (SPE). For rice and hulled rice samples, n-hexane partition was additionally adopted to remove nonpolar interferences, mainly lipids. The residue levels were analyzed by HPLC with DAD, using $C_8$ column. LOQ (limit of quantitation) of pymetroizinie was 1 ng (S/N > 10) and MQL (method quantitation limit) was 0.01 mg/kg. Mean recoveries from 11 crop samples fortified at three levels (MQL, 10 ${\times}$ MQL and 50 ${\times}$ MQL) in triplicate were in the range of 83.1~98.5% with coefficients of variation (CV) of less than 10%, regardless of sample type, which satisfies the criteria of KFDA. The method established in this study could be applied to most of crops as an official and general method for analysis of pymetrozine residue.

• The Korean Journal of Pesticide Science
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• v.17 no.2
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• pp.84-93
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• 2013

Validated analytical methods for isopyrazam are meager or lacking. In the present study, a single residual analytical method was developed for isopyrazam in selected commodities. Isopyrazam was analyzed in brown rice, soybean, green pepper, mandarin, cucumber, and Korean melon. We tried different solvents and methods through extraction, partition and purification steps to obtain best analytical results. For isopyrazam samples were extracted with acetonitrile, concentrated and partitioned with n-hexane, clean-up using florisil with n-hexane/ethylacetate (70/30) and analyzed with HPLC/UVD. The limit of quantitation (LOQ) for isopyrazam was 1.0 ng (S/N > 10) and method LOQ (MLOQ) was 0.04 mg $kg^{-1}$. Recovery ranged through 81.0~105.3% (syn-isomer) and 80.8~105.6% (anti-isomer) at fortification level of 0.04 (MLOQ), 0.4 (10 ${\times}$ MLOQ), and 2.0 (50 ${\times}$ MLOQ). The coefficient of variation (CV) for isopyrazam was less than 10% regardless of sample types. These results were further confirmed with LC/MS, respectively. The proposed method is highly reproducible and sensitive and is suitable for routine analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.434-441
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• 2016

Friction Stir Welding (FSW) is a solid-state joining process involving the frictional heat between the materials and tools. The amount of heat conducted into the workpiece determines the quality of the welded zone. Excessive heat input is the cause of oxides and porosity defects, and insufficient heat input can cause problems, such as tunnel defects. Therefore, analyzing the temperature history and distribution at the center of the Friction Stir Welded zone is very important. In this study, the temperature distribution of the friction stir welding region of an AZ61 magnesium alloy was investigated. To achieve this goal, the temperature and metal flow was predicted using the finite element method. In FE analysis, the welding tool was simplified and the friction condition was optimized. Moreover, the temperature measuring test at the center of the welding region was performed to verify the FE results. In this study, the tool rotation speed was a more dominant factor than the welding speed. In addition, the predicted temperature at the center of the welding region showed good agreement with the measurement results within the error range of 5.4% - 7.7%.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.12
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• pp.2125-2134
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• 2000

This research was to develope the economical treatment processes of the landfill leachate to meet the legal discharge standards. To achieve this purpose, experiments were conducted in laboratory to choose the optimum process and to obtain the design factors before a pi!ot-scale test. The concept of the process developing in this research was using the reverse osmosis system. The submerged membrane bio-reactor was used to achieve pre-treatment of reverse osmosis system and the concentrate was treated by evaporator with land fill gas as a fuel. The results of the research showed that SS, $BOD_5$, $COD_{cr}$, $NH_4{^+}-N$ and T-N were removed 99.0%, 43.0%, 12.9%, 48.5% and 18.7% respectively in the submerged membrane bio-reactor. The reverse osmosis system could remove $BOD_5$, $COD_{cr}$, $NH_4{^+}-N$ and T-N as an efficiency of97.5%, 97.6%, 79.7% and 85.4% respectively. The evaporator could remove $COD_{cr}$, $NH_4{^+}-N$ and T-N as an efficiency of 90.5%, 50.6% and 63.3% respectively. However the condensed water of the evaporator was not satisfied the legal standard and should be treated in reverse osmosis with the pre-treated leachate.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.1
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• pp.97-104
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• 2010

At present, the biological nutrient removal (BNR) process for removal of nitrogen and phosphorus is being constructing to keep pace with the reinforced standard of effluent quality and the traditional activated sludge process of preexistence is being promoting to retrofit. At the most case of retrofitting, processes are subjected to be under consideration as alternative BNR process for retrofitting. However, process evaluation methods are restricted to compare only treatment efficiency. Therefore, when BNR process apply, process evaluation was needed various method for treatment efficiency as well as sludge production and aeration cost, and all. In this study, the evaluation method of alternative process was suggested for the case for retrofitting S wastewater treatment plant which has been operated the standard activated sludge process. Three BNR processes for evaluation of proper alternatative process were selected and evaluated with suggested method. The selected $A^2$/O, VIP and DNR processes were evaluated using the mathematical model which is time and cost effective as well as gathered objective evaluation criteria. The evaluation between 5 individual criteria was possible including sludge production and energy efficiency as well as treatment performance. The objective final decision method for selection of optimal process was established through the fuzzy inference.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.588-595
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• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of Korean Society of Transportation
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• v.17 no.3
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• pp.61-70
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• 1999

The Purpose of this study is to test whether Braess Paradox (BP) can be revealed in a real world network. Fer the study, Namsan 2nd tunnel case is chosen, which was shut down for 3 years for repair works. The revelation of BP is determined by analyzing network-wise traffic impacts followed by the tunnel closure. The analysis is conducted using a network simulation model called SECOMM developed for the congestion management of the Seoul metropolitan area. Also, the existence of BP is further identified by a before-after traffic survey result of the major arterials nearby the Namsan 2nd tunnel. The model estimation expected that the closure of Namsan 2nd tunnel improve the network-wise average traffic speed from 21.95km/h to 22.21km/h when the travel demand in the study area and congestion Pricing scheme on Namsan 1st & 3rd tunnels remain unchanged. In addition, the real world monitoring results of the corridors surrounding Namsan 2nd tunnel show that the average speed increases from 29.53km/h to 30.37km/h after the closure. These findings clearly identify the BP Phenomenon is revealed in this case.

• Korean Journal of Plant Resources
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• v.33 no.5
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• pp.419-427
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• 2020

Ligusticum chuanxiong Hort. is included in Umbelliferae family, it is one of the Korean traditional medicinal plants as the roots have been used to treat diseases. In this study, the growth characteristics and active compound contents of L. chuanxiong were compared among the different forest sites. As a result, root diameter and root length of L. chuanxiong was the highest in Jeongseon. Also, the fresh weight and dry weight of L. chuanxiong were the highest in Jeongseon. The total content of active compound was 23.27 mg/g the highest in Bonghwa, and 21.59 mg/g in Jeongseon, 15.87 mg/g in Hamyang was accumulated. In this study compares three forest site for cultivating of L. chuanxiong in different climate zone that the best site to product yield were Jeongseon. In this sites were located in higher altitue and lower temperature than other sites, also there were shown that lower soil moisture contents and well-drained soil. It was shown yield and active compound contents of L. chuanxiong was influenced by micro-environment conditions like as altitude, temperature, soil conditions.