• Economic and Environmental Geology
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• v.43 no.6
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• pp.589-601
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• 2010

Recently it has been frequently reported arsenic contamination of geologic origin in groundwater. The iron-impregnated ranular activated carbon (Fe-GAC) was developed for effective removal of arsenic from groundwater n the study. Fe-GACs were prepared by impregnating iron compounds into a supporting medium (GAC) with 0.05 M iron nitrate solution. The materials were used in arsenic adsorption isotherm tests to know the effect of iron impregnation time, batch kinetic tests to understand the influence of pH, and column tests to evaluate for the preliminary operation of water treatment system. The results showed that the minimum twelve hours of impregnation time were required for making the Fe-GAC with sufficient iron content for arsenic removal, confirmed by a high arsenic adsorption capacity evaluated in the isotherm tests. Most of the impregnated iron compounds were iron hydroxynitrate $Fe_4(OH)_{11}NO_3{\cdot}2H_2O$ but a mall quantity of hematite was also identified in X-ray diffraction(XRD) analysis. The batch isotherms of Fe-GAC for arsenic adsorption were well explained by Langmuir than Freundlich model and the iron contents of Fe-GAC have positive linear correlations on logarithmic plots with Freundlich distribution coefficients ($K_F$ and Langmuir maximum adsorption capacities ($Q_m$. The results of kinetic experiments suggested hat Fe-GAC had he excellent arsenic adsorption capacities regardless of all pH conditions except for pH 11 and could be used a promising adsorbents for groundwater arsenic removal considering the general groundwater pH range of 6-8. The pseudo-second order model, based on the assumption that the ate-limiting step might be chemisorption, provided the best correlation of the kinetic experimental data and explained the arsenic adsorption system f Fe-GAC. The column test was conducted to valuate the feasibility of Fe-GAC use and the operation parameters in arsenic groundwater treatment system. The parameters obtained from the column test were the retardation actor of 482.4 and the distribution coefficient of 581.1 L/mg which were similar values of 511.5-592.5 L/mg acquired from Freundlich batch isotherm model. The results of this study suggested that Fe-GAC could be used as promising adsorbent of arsenic removal in a small groundwater supply system with water treatment facility.

• Archives of design research
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• v.17 no.1
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• pp.339-346
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• 2004

Matrix, produced by Larry & Andy Wachowski, was relatively precisely dealt with the cyber world. After the movie was released, it had a mania for the movie and was adopted into a various forms of cultural products. It was remade not only into the parodies of the other movies and TV programs, but also the clothes and miscellaneous items of the movie were reincarnated as an unique cultural trend. The cause of the popularity is the fresh storyline as well as the sophisticated visual effects and good-looking actors. The agony of the protagonist was connected with the people outside the movie who are yearning for the ideal world. He was confused at the fact that his circumstances which were believed as the real world were not tortally true, complicated between the sensually phisical truth and the spiritual truth and had an will for the freedom that would ransack the truth and save the other people from the fictitious world. Consequently, the movie has got sympathies with many audiences suggesting the situation that has no a firm belief of the reality, the difference between the real and the cyber world is meaningless and the faked images of the high-technology are overturned This thesis tries to study the present that the real images are excessly duplicated and consumed, related to the Jean Baudrillard's theory, 'Hyperreel'. Replaced the real objects by a technical programming in the Matrix world, there happens the image-violence that the true nature is slaughterred by images. In the world where the reproducts are more actual than the reality and pretends to be real, only semiotics are consumed and produced. That is to say, the tortally programmed images has no references and aims, therefore should be produced in an 'impediment-strategy' like a faked crisis. That is the step of 'Simulation' that artificially reincarnates the real. Based upon the Baudrillard's theory, 'Simulacre', this study tries to research today's post-modern situation that the boundary of the real world and the faked copy is vague and vanishing, through the analysis of the cyber world of the movie 'Matrix'.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Korean Journal of Food Science and Technology
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• v.50 no.3
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• pp.331-338
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• 2018

The purpose of this study was to determine the optimum sterilization conditions for the production of retorted steamed egg using response surface methodology. Sterilization processes for eighteen conditions using varying sterilization temperature ($X_1$), time ($X_2$), and method ($X_3$) as the independent variables were carried out through a $3^2{\times}2$ experimental factorial design. Quality evaluations after sterilization included measurements of $F_0$ value ($Y_1$), peak stress ($Y_2$), pH ($Y_3$), color value ($Y_{4-6}$), and organoleptic test [preference for appearance ($Y_7$), overall acceptability ($Y_8$), and preference for texture ($Y_9$) and egg taste ($Y_{10}$)]. Dependent variables ($Y_{1-10}$) of eighteen conditions were more affected by temperature and time than by the sterilization method. Eight factors were selected among the dependent variables as significant factors related to the quality of the steamed egg. Finally, by establishing an optimum range of each dependent variable and contour analysis, the optimum sterilization conditions for the production of steamed egg were determined to be $120^{\circ}C$ for 25 min using a 2-step sterilization process.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.187-198
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• 2019

The purpose of this study is to seek ways of utilizing TLBMC by understanding business model of social ventures that are accompanied by social and environmental as well as economic missions. In order to achieve this, business models from economic, environmental and social perspectives will be analyzed, and we seek to enhance sustainability of social venture entrepreneurs. As the analysis tool, TLBMC(Triple Layered Business Model Canvas) expands upon the business model canvas that is widely utilized and recognizes economical terms. The TLBMC is proposed by Joyce, A., & Paquin, R. L.(2016) to help achieve a holistic view with horizontal and vertical associations. The study tries to overcome limitations of previous studies and observe a variety of economic, environmental, and social perspectives that social ventures should have with the TLBMC. As a result, it has the following implications; Firstly, creating separate social, environmental and economic business model canvas helps a business to have a holistic approach. Secondly, it was found that social venture characteristics of environmental and social perspectives were applied in practice. Lastly, we have established experience data on social venture business model. This study focuses on the opinions, the meanings and the subjective views of the participants. As a result, conclusions are drawn by the researchers ' assertions and has limitations as a research on case studies. However, this study will help people who are preparing or studying social ventures to have economic, environmental, and social perspectives. Also, redefinition of the direction and value of entrepreneurs operating social ventures, such as vision and mission, will help clarify the roles and responsibility of organizations. As a fundamental step for future empirical studies, this study could be the base for social venture business model studies.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.101-114
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• 2019

In this study, we carry out the numerical simulation to trace the yearly shoreline change of Mang-Bang beach, which is suffering from erosion problem. We obtain the basic equation (One Line Model for shoreline) for the numerical simulation by assuming that the amount of shoreline retreat or advance is balanced by the net influx of longshore and cross-shore sediment into the unit discretized shoreline segment. In doing so, the energy flux model for the longshore sediment transport rate is also evoked. For the case of cross sediment transport, the modified Bailard's model (1981) by Cho and Kim (2019) is utilized. At each time step of the numerical simulation, we adjust a closure depth according to pertinent wave conditions based on the Hallermeier's analytical model (1978) having its roots on the Shield's parameter. Numerical results show that from 2017.4.26 to 2017.10.15 during which swells are prevailing, a shoreline advances due to the sustained supply of cross-shore sediment. It is also shown that a shoreline temporarily retreats due to the erosion by the yearly highest waves sequentially occurring from mid-October to the end of October, and is followed by gradual recovery of shoreline as high waves subdue and swells prevail. It is worth mentioning that great yearly circulation of shoreline completes when a shoreline retreats due to the erosion by the higher waves occurring from mid-March to the end of March. The great yearly circulation of shoreline mentioned above can also be found in the measured locations of shoreline on 2017.4.5, 2017.9.7, 2017.11.7, 2018.3.14. However, numerically simulated amount of shoreline retreat or advance is more significant than the physically measured one, and it should be noted that these discrepancies become more substantial for the case of RUN II where a closure depth is sustained to be as in the most morphology models like the Genesis (Hanson and Kraus, 1989).

• Applied Chemistry for Engineering
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• v.29 no.6
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• pp.710-715
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• 2018

In this study, $ZnFe_2O_4@SnO_2@TiO_2$ core-shell nanoparticles (NPs), a photocatalytic material with magnetic properties, were synthesized through a three-step process. Structural properties were investigated using X-ray diffraction (XRD) analysis. It was confirmed that $ZnFe_2O_4$ of the spinel, $SnO_2$ of the tetragonal and $TiO_2$ of the anatase structure were synthesized. The magnetic properties of synthesized materials were studied by a vibrating sample magnetometer (VSM). The saturation magnetization value of $ZnFe_2O_4$, a core material, was confirmed at 33.084 emu/g. As a result of the formation of $SnO_2$ and $TiO_2$ layers, the magnetism due to the increase in thickness was reduced by 33% and 40%, respectively, but sufficient magnetic properties were reserved. The photocatalytic efficiency of synthesized materials was measured using methylene blue (MB). The efficiency of the core material was about 4.2%, and as a result of the formation of $SnO_2$ and $TiO_2$ shell, it increased to 73% and 96%, respectively while maintaining a high photocatalytic efficiency. In addition, the antibacterial activity was validated via the inhibition zone by using E. Coli and S. Aureus. The formation of shells resulted in a wider inhibition zone, which is in good agreement with photocatalytic efficiency measurements.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.18-35
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• 2019

The Korea District Heating Corporation operates a gas engine generator with a capacity of $4500m^3 /day$ of biogas generated from the sewage treatment plant of the Nanji Water Recycling Center and 1,500 kW. However, the actual operation experience of the biogas power plant is insufficient, and due to lack of accumulated technology and know-how, frequent breakdown and stoppage of the gas engine causes a lot of economic loss. Therefore, it is necessary to prepare technical fundamental measures for stable operation of the power plant In this study, a series of process problems of the gas engine plant using the biogas generated in the sewage treatment plant of the Nanji Water Recovery Center were identified and the optimization of the actual operation was made by minimizing the problems in each step. In order to purify the gas, which is the main cause of the failure stop, the conditions for establishing the quality standard of the adsorption capacity of the activated carbon were established through the analysis of the components and the adsorption test for the active carbon being used at present. In addition, the system was applied to actual operation by applying standards for replacement cycle of activated carbon to minimize impurities, strengthening measurement period of hydrogen sulfide, localization of activated carbon, and strengthening and improving the operation standards of the plant. As a result, the operating performance of gas engine # 1 was increased by 530% and the operation of the second engine was increased by 250%. In addition, improvement of vent line equipment has reduced work process and increased normal operation time and operation rate. In terms of economic efficiency, it also showed a sales increase of KRW 77,000 / year. By applying the strengthening and improvement measures of operating standards, it is possible to reduce the stoppage of the biogas plant, increase the utilization rate, It is judged to be an operational plan.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Economic and Environmental Geology
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• v.52 no.3
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• pp.251-258
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• 2019

During the detailed geological survey around the southern Yangsan Fault, we newly found a Quaternary fault outcrop, which cuts unconsolidated sediments. The fault named the Sinwoo site, located in the Sinwoo pasture, Miho-ri, Duseo-myeon, Ulsan metropolitan city, is the first discovered Quaternary fault near the western part of the south Yangsan Fault. In this study, we provide information on characteristics of fault geometry and unconsolidated sediment at Sinwoo site based on the analysis data of topography, drainage, and lineament around the study site. The fault site is situated at pediment slope, but fan-shaped middle terrace, as well as thick sediment exposed at low terrace, indicates that the unconsolidated sediments have been deposited in the alluvial fan environment. The drainage develops to the third-order drainage system, and the first and the second drainage system meet at right angles to each other and form a radial drainage pattern. In addition, the NE-SW direction lineaments can be identified on the basis of the curvature of the river and the step of the topographic relief, running over the Sinwoo site. The fault of $N30-35^{\circ}E/79-82^{\circ}SE$ shows ~ 5.8 m apparent vertical offset and dominantly reverse-slip sense based on slickenline, rotation of pebbles, and drag folding at footwall. However, some discontinuous sediments observed in the footwall are interpreted as fissure-filling materials due to the strike-slip movement. Now, we are under multidisciplinary investigations of additional field survey and age dating in order to determine the evolution of Sinwoo site fault during the Quaternary.