Search results for: Seoung Eun Huh

Authors: Haragobinda Srichandan, Ashish Pathak, Dong Jin Kim, Seoung-Won Lee

Abstract:

The present investigation deals with bioleaching of spent petroleum catalyst using At. ferrooxidans and At. thiooxidans. The spent catalyst used in the present study was pretreated with acetone to remove the oily hydrocarbons. FESEM and XPS analysis indicated the presence of metals in sulfide and oxide forms in spent catalyst. Both At. ferrooxidans and At. thiooxidans were found to be highly effective in producing the acid. Bioleaching with At. ferrooxidans and At. thiooxidans led to higher recovery of metals compare to control. During bioleaching similar recoveries of metals were obtained using At. ferrooxidans and At. thiooxidans. This might be due to the presence of metals as soluble oxides and sulphides in the spent catalyst. At the end of bioleaching, about 87-90% Ni, 34% Al, 65-73% Mo and 92-97% V were leached using above bacteria. It is elucidated that bioleaching with At. thiooxidans is comparatively more advantageous due to lower cost of sulphur.

Keywords: At. ferrooxidans, bioleaching, metal recovery, spent catalyst

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Authors: Hyeon-seoung Kim, Sang-mi Park, Sun-ju Han, Leen-seok Kang

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The visualization technology of construction information using 3D and nD modeling can satisfy the visualization needs of each construction project participant. The nD CAD system is a tool that the construction information, such as construction schedule, cost and resource utilization, are simulated by 4D, 5D and 6D object formats based on 3D object. This study developed a methodology and simulation engine for nD CAD system for construction project management. It has improved functions such as built-in schedule generation, cost simulation of changed budget and built-in resource allocation comparing with the current systems. To develop an integrated nD CAD system, this study attempts an integrated method to link 5D and 6D objects based on 4D object.

Keywords: building information modeling, visual simulation, 3D object, nD CAD augmented reality

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Authors: Haragobinda Srichandan, Ashish Pathak, Dong Jin Kim, Seoung-Won Lee

Abstract:

The present investigation deals with bioleaching of spent refinery catalyst (as received) using At. thiooxidans. The effect of substrate concentration and pulp density was studied. XPS analysis concluded that the metals in spent catalyst were present as both sulfide and oxides. The dissolution behavior of metals during bioleaching was different. During bioleaching, higher dissolution of Ni and lower dissolution of Mo, V and Al was observed. An increase in pulp density from 1% to 10% led to a decrease in leaching yields of all the metals. This was due to the substantial increase in medium pH at higher pulp densities. The maximum negative impact of pulp density was observed on the leaching yield of V. An increase in sulfur concentration from 0.5% to 2.5% didn’t bring positive impact on metal leaching yield. 0.5% sulfur was found to be the optimum above which no significant increase in leaching yields of metals was observed.

Keywords: At. thiooxidans, pulp density, spent catalyst, bioleaching

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Authors: Woo-tai Jung, Jong-sup Park, Jae-yoon Kang, Moon-seoung Keum

Abstract:

CFRP (Carbon Fiber Reinforced Polymer) is mainly used as reinforcing material for degraded structures owing to its advantages including its non-corrodibility, high strength, and lightweight properties. Recently, dedicated studies focused not only on its simple bonding but also on its tensioning. The tension necessary for prestressing requires the anchoring of multi-CFRP tendons with high capacity and the surface treatment of the CFRP tendons may also constitute an important issue according to the type of anchor. The wedge type, swage type or bonded type anchor can be used to anchor the CFRP tendon. The bonded type anchor presents the disadvantage to lengthen the length of the anchor due to the low bond strength of the CFRP tendon without surface treatment. This study intends to overcome this drawback through the application of a method enlarging the bond area at the end of the CFRP tendon. This method enlarges the bond area by splitting the end of the CFRP tendon along its length and can be applied when CFRP is produced by pultrusion. The application of this method shows that the mono-CFRP tendon and 3-multi CFRP tendon secured the anchor performance corresponding to the tensile performance of the CFRP tendon and that the 7-multi tendon secured anchor performance corresponding to 90% of the tensile strength due to the occurrence of buckling in the steel tube anchorage.

Keywords: carbon fiber reinforced polymer (CFRP), tendon, anchor, tensile property, bond strength

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Authors: Hankyu Lee, Seoung Bum Kim

Abstract:

The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Young Jae Choung, Seoung Bum Kim

Abstract:

Statistical Process Control (SPC) is a popular technique for process monitoring. A widely used tool in SPC is a control chart, which is used to detect the abnormal status of a process and maintain the controlled status of the process. Traditional control charts, such as Hotelling’s T2 control chart, are effective techniques to detect abnormal observations and monitor processes. However, many complicated manufacturing systems exhibit nonlinearity because of the different demands of the market. In this case, the unregulated use of a traditional linear modeling approach may not be effective. In reality, many industrial processes contain the nonlinear and time-varying properties because of the fluctuation of process raw materials, slowing shift of the set points, aging of the main process components, seasoning effects, and catalyst deactivation. The use of traditional SPC techniques with time-varying data will degrade the performance of the monitoring scheme. To address these issues, in the present study, we propose a parameterless self-organizing map (PLSOM)-based control chart. The PLSOM-based control chart not only can manage a situation where the distribution or parameter of the target observations changes, but also address the nonlinearity of modern manufacturing systems. The control limits of the proposed PLSOM chart are established by estimating the empirical level of significance on the percentile using a bootstrap method. Experimental results with simulated data and actual process data from a thin-film transistor-liquid crystal display process demonstrated the effectiveness and usefulness of the proposed chart.

Keywords: control chart, parameter-less self-organizing map, self-organizing map, time-varying property

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Authors: Ae Ra Han, Seoung Eun Huh, Ji Yeon Kim, Joanne Kwak-Kim, Sung Ki Lee

Abstract:

Objective: Prevalence of osteoporosis, cardiovascular disorders, and Alzheimer's disease rapidly increase after menopause. Immune activation and inflammation are suggested as an important pathogenesis of these serious diseases. Several pro-inflammatory cytokines are increased in women with surgical or natural menopause. However, the little is known about IL-17 producing T cells and Foxp3+ regulatory T (Treg) cells in post-menopause. Methods: A total of 34 postmenopausal women, who had no active cardiovascular, endocrine and infectious disorders were recruited as study group and healthy premenopausal women participated as controls. Peripheral blood mononuclear cells were isolated. Immuno-morphologic (CD3, CD4, CD8, CD19, CD56/CD16), intracellular cytokine (TNF-alpha, IFN-gamma, IL-10, IL-17), and Treg cell (Foxp3) studies were carried out using flow cytometry. The proportion of peripheral lymphocytes, including IL-17 producing and Foxp3+ Treg cells immune cell in each group were statistically analyzed. Results: The proportion of NK cells was significantly increased in menopausal women as compared to that of controls (P=.005). The ratios of TNF-alpha/IL-10 producing CD3+CD4+ T cells were increased in postmenopausal women. CD3+IL-17+ T cell level was higher in postmenopausal women and CD4+ Foxp3+ Treg cells was lower than that of controls. The ratios of CD3+IL-17+ T cell to CD3+Foxp3+ and to CD4+Foxp3+ Treg cells were significantly increased in postmenopausal women (P=.001). Conclusions: We found enhanced innate immunity and Th1- and Th17-mediated adaptive immunity in postmenopausal women. This may explain increasing prevalence of chronic inflammatory diseases after menopause. Further studies are needed to elucidate what factors contribute to this inflammatory shift in the postmenopause.

Keywords: inflammation, immune cell, menopause, Th17, regulatory T cell

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Authors: Jaehong Yu, Seoung Bum Kim

Abstract:

Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Seulki Lee, Seoung Bum Kim

Abstract:

Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Younghoon Kim, Seoung Bum Kim

Abstract:

One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

Procedia PDF Downloads 151