• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.1463-1474
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• 2008

In order to investigate stabilization effect and sustainability on As-contaminated farmland soils which were affected the abandoned mine site and stabilized by zerovalent iron(ZVI) and industrial by-products, batch-scale and pilot-scale tests were carried out. In batch tests, ZVI and industrial by-products(blast furnace slag, steel refining slag and oyster shell powder) were used in treatment materials to reduce the As leaching. Industrial by-products were mixed with As-contaminated soils, in the ratio of 1%, 3%, 5% and 7% on the weight base of dried soil. The results of batch-scale tests was shown that the reduction of As concentration was observed in all samples and it was expected that ZVI and steel refining slag were more effective than other treatment materials to stabilize As compounds. In pilot-scale tests, columns were filled with untreated soils and treated soils mixed with ZVI and steel refining slag in the same mixing ratio of 3%. Distilled water was discharged into the columns with the velocity of 0.3 pore volume/day. During the test, pH, EC, Eh and As concentration were measured in the regular term(1pore volume). after six months, pilot-scale tests were retested to investigate sustainability of treatment materials. As a result, It was shown that the leachate from control column was continuously released during the test period and its concentration was greater than $100ug{\cdot}L^{-1}$ which was exceeded the national regulation of water discharged to river or stream ($50ug{\cdot}L^{-1}$). On the other hand, soil treated with ZVI and steel refining slag showed that the concentrations of leachate were lower than national regulation of water discharged to river or stream. Therefore it was expected that ZVI and steel refining slag could be applied to the farmland site as the alternative treatment materials.

• Journal of the Korean Institute of Gas
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• v.21 no.3
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• pp.1-8
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• 2017

The purpose of this study is to suggest the rupture disk design(size) and application at the two phase(gas-liquid) flow by runaway reaction at batch reactor. The definition of runaway reaction is abnormally exothermic reaction by the uncontrolled cooling water or deviated operating condition. As a result, the temperature of reactor is rapidly increasing. The causes of runaway reaction are either self-heating reaction or sleeper reaction. General methods of rupture disk size or safety valve are not suitable in the runaway reaction, because of temperature and pressure increasing rapidly in the reactor and the phases of relieving fluid is 2-phase flow. This study case of the reactor incident, the depressurization system such as safety valve and vent installed, however, the system did not relieved the pressure of reactor suitably. The orifice size of the safety valve were designed too small because the size had not been considered the phenomena and character of reaction. The batch reactor design should be considered by referring to the possibility of runaway reaction proposed in this study and the size of rupture disk design method considering 2-phase flow.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4261-4266
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• 2011

Polypropylene (PP)/talc compounds with talc content of 20wt% were fabricated by master-batch (MB) and direct compounding method using injection molding. The MB was prepared by mini compounder at $200^{\circ}C$ and the content of talc was 50wt%. The talc dispersion of the PP/talc compound was investigated by SEM-EDS. The talc was well dispersed within PP matrix in case of the MB-PP compound using MB. The rheological properties of the PP/talc compounds were measured by dynamic Rheometer. The MB-PP compound indicates higher shear thinning and elastic property than direct compound. The disperion of talc was certified by G'-G" plot, and Van Gurp-Palmen analysis was applied in order to certify an increase in elasticity.

• Korean Journal of Materials Research
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• v.34 no.7
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• pp.341-354
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• 2024

Today, the principles of green chemistry are being fundamentally applied in the chemical industry, such as the nitrobenzene industry, which is an essential intermediate for various commercial products. Research on the application of response surface methodology (RSM) to optimize nitrobenzene synthesis was conducted using a sulfated silica (SO₄/SiO₂) catalyst and batch microwave reactor. The nitrobenzene synthesis process was carried out according to RSM using a central composite design (CCD) design for three independent variables, consisting of sulfuric acid concentration on the silica (%), stirring time (min), and reaction temperature (℃), and the response variable of nitrobenzene yield (%). The results showed that a three-factorial design using the response surface method could determine the optimum conditions for obtaining nitrobenzene products in a batch microwave reactor. The optimum condition for a nitrobenzene yield of 63.38 % can be obtained at a sulfuric acid concentration on the silica of 91.20 %, stirring time of 140.45 min, and reaction temperature of 58.14 ℃. From the 20 experiments conducted, the SO₄/SiO₂ catalyst showed a selectivity of 100 %, which means that this solid acid catalyst can potentially work well in converting benzene to nitrobenzene.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.585-587
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• 2023

Long-tailed datasets have an imbalanced distribution because they consist of a different number of data samples for each class. However, there are problems of the performance degradation in tail-classes and class-accuracy imbalance for all classes. To address these problems, this paper suggests a learning method for training of long-tailed dataset. The proposed method uses and combines two methods; one is a resampling method to generate a uniform mini-batch to prevent the performance degradation in tail-classes, and the other is a reweighting method to address the accuracy imbalance problem. The purpose of our proposed method is to train the learning models to have uniform accuracy for each class in a long-tailed dataset.

• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.719-725
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• 2020

Objectives: With the growth of national interest in fine particulate matter, many complaints about pollutants emitted from air pollution emitting facilities have arisen in recent years. In particular, it is thought that a large volume of particulate pollutants are discharged from workplaces that use Solid Refuse Fuel (SRF). Therefore, particulate contaminants generated from SRF were measured and analyzed in this study in terms of respective particle sizes. Methods: In this study, particulate matter in exhaust gas was measured by applying US EPA method 201a using a cyclone. This method measures Filterable Particulate Matter (FPM), and does not consider the Condensable Particulate Matter (CPM) that forms particles in the atmosphere after being discharged as a gas in the exhaust gas. Results: The mass concentration of Total Suspended Particles (TSP) in the four SRF-using facilities was 1.16 to 11.21 mg/Sm3, indicating a very large concentration deviation of about 10 times. When the fuel input method was the continuous injection type, particulate matter larger than 10 ㎛ diameter showed the highest particle size fraction, followed by particulate matter smaller than 10 ㎛ and larger than 2.5 ㎛, and particulate matter of 2.5 ㎛ or less. Contrary to the continuous injection type, the batch injection type had the smallest particle size fraction of particulate matter larger than 10 ㎛. The overall particulate matter decreased as the operating load factor decreased from 100% to 60% at the batch input type D plant. In addition, as incomplete combustion significantly decreased, the particle size fraction also changed significantly. Both TSP and heavy metals (six items) satisfied the emissions standards. The measured value of the emission factor was 38-99% smaller than the existing emissions factor. Conclusions: In the batch injection facility, the particulate matter decreased as the operating load factor decreased, as did the particle size fraction of the particulate matter. These results will help the selection of effective methods such as reducing the operating load factor instead of adjusting the operating time during emergency reduction measures.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.867-873
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• 2001

The purpose of this study is to find analytic solution of determining the optimal capacity (lot-size) of multiproduct acyclic multistage production and inventory system to meet the finished product demand under the constraint of finite intermediate storage. Intermediate storage is a practical way to mitigate the material flow imbalance through the line of supply and demand chain. However, the cost of constructing and operating storage facilities is becoming substantial because of increasing land value, environmental and safety concern. Therefore, reasonable decision-making about the capacity of processes and storage units is an important subject for industries. The industrial solution for this subject is to use the classical economic lot sizing method, EOQ/EPQ(Economic Order Quantity/Economic Production Quantity) model, incorporated with practical experience. But EOQ/EPQ model is not suitable for the chemical plant design with highly interlinked processes and storage units because it is developed based on single product and single stage. This study overcomes the limitation of the classical lot sizing method. The superstructure of the plant consists of the network of serially and/or parallelly interlinked non-continuous processes and storage units. The processes transform a set of feedstock materials into another set of products with constant conversion factors. A novel production and inventory analysis method, PSW(Periodic Square Wave) model, is applied to describe the detail material flows among equipments. The objective function of this study is minimizing the total cost composed of setup and inventory holding cost. The advantage of PSW model comes from the fact that the model provides a set of simple analytic solutions in spite of realistic description of the material flows between processes and storage units. the resulting simple analytic solution can greatly enhance the proper and quick investment decision for the preliminary plant design problem confronted with economic situation.

• Journal of Korean Library and Information Science Society
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• v.48 no.3
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• pp.233-257
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• 2017

In this paper, we propose an effective method to convert and construct the MEDLINE, the world's largest biomedical bibliographic database, into linked data. To do this, we first derive the appropriate RDF schema by analyzing the MEDLINE record structure in detail, and convert each record into a valid RDF file in the derived schema. We apply the dual batch registration method to streamline the subject URI duplication checking procedure when merging all RDF files in the converted record unit and storing it in a single RDF triple storage. By applying this method, the number of RDF triple storage accesses for the subject URI duplication is reduced from 26,597,850 to 2,400, compared with the sequential configuration of linked data in units of RDF files. Therefore, it is expected that the result of this study will provide an important opportunity to eliminate the inefficiency in converting large volume bibliographic record sets into linked data, and to secure promptness and timeliness.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.1-19
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• 2010

Recently, it is necessary to process real time sensor data, which is generated from ubiquitous environments. Data, which are written by XML, are small, but, large volumes of data. Therefore, weneed to use an efficient method for processing a large amount of it. An XQuery has two types for sensor data: one is to get sensor identification and value from sensor data; the other is restructuring for user's convenience. Existing XQuery engines don't have efficient method for batch processing of sensor data. This paper proposed the twig query processing over reverse path summary, and we developed and applied restructuring batch processing method for real time processing of a large amount of sensor data. Finally, we do performance evaluation using XMark and RFID EPC data, and comparison analysis with MonetDB/XQuery and Berkeley DB XML.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.3
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• pp.280-285
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• 2004

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.