• Journal of Korean Philosophical Society
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• v.147
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• pp.239-260
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• 2018

Is an artificial intelligence robot, a non-human beings newly emerging in the age of technology, a threat to human beings, or a mutual cooperation or ensemble with human beings? The desire to control nature through the use of the power of science and technology is manifested in the fear that humans can annihilate themselves. This study attempts to identify the problems of Cartesian epistemology underlying these questions and fears and to answer these questions based on Merleau - Ponty 's ontological ontology using the Ontology and Latour' s ontology and technological philosophy. The cogito derived from the Cartesian philosophy became the basis of the structure of dichotomous epistemology of 'subjectivity and objectivity' based on human - reason. In the human-centered world, all non-human beings were tools or controls for humans. The problem of the modern people is not only to get help from the natural scientific methods to control the nature including man, but also to think that scientific method is the only way to understand the world. In criticizing this, Merleau-Ponty shows that the body mediates between human beings and non-human beings, and provides a possible ontological basis for the ontology. Merleau - Ponty 's phenomenological methodology and ontology are newly developed by Simondon under the influence of phenomenological philosopher and phenomenology. The relationship between human beings and nonhuman beings by Simondon appears as an ensemble of human and technical objects or a mutual co - operation of human and technical objects. In particular, Latour goes a step further in Simondon and defines all the bodies living in the world as actor-network theory, denying the core concept of modernity. Merleau - Ponty 's phenomenological view can be a new possible basis for the philosophical discussion of the technological age. We will see that the problem itself can be solved by shifting modern fear to a phenomenological attitude.

• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.657-668
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• 2022

With the occurrence of localized heavy rain while river flow has increased, both flow and rainfall cause riverside flood damages. As the degree of damage varies according to the level of social and economic impact, it is required to secure sufficient forecast lead time for flood response in areas with high population and asset density. In this study, the author established a flood risk matrix using ensemble rainfall runoff modeling and evaluated its applicability in order to increase the damage reduction effect by securing the time required for flood response. The flood risk matrix constructs the flood damage impact level (X-axis) using flood damage data and predicts the likelihood of flood occurrence (Y-axis) according to the result of ensemble rainfall runoff modeling using LENS rainfall data and as well as probabilistic forecasting. Therefore, the author introduced a method for determining the impact level of flood damage using historical flood damage data and quantitative flood damage assessment methods. It was compared with the existing flood warning data and the damage situation at the flood warning points in the Taehwa River Basin and the Hyeongsan River Basin in the Nakdong River Region. As a result, the analysis showed that it was possible to predict the time and degree of flood risk from up to three days in advance. Hence, it will be helpful for damage reduction activities by securing the lead time for flood response.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.751-759
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• 2023

Breast ultrasonography is difficult to image in fatty breasts and to find micro-calcification, but the discovery of micro-calcification is very important for breast cancer screening. Among the color Doppler artifact of ultrasound, twinkle artifact mainly occur on strong reflectors such as stones or calcification in images, and evaluation methods using them are clinically being used. In this study, we are conducting experiments on the color Doppler settings of ultrasound equipment, such as repetition frequency, ensemble, persist, wall filtering, smoothing, linear density, and dissociation value, by producing a breast simulation phantom using the largest amount of calcium phosphate among breast implants. The purpose of this study was to improve the contrast of twinkle artifact in breast ultrasound examinations and to maximize their use in clinical practice. As a result, the pulse repetition frequency occurred in the range of 3.6 kHz to 7.2 kHz, and did not occur above 10.5 kHz. For ensembles, twinkle artifact occurred in all sizes of calcification under low conditions, and in threshold settings, the twinkle artifact increased slightly only under 80 to 100 conditions, and did not occur in 1 mm size calcification. Persist, wall filter, smoothing, and line density settings did not have much meaning in the setting variable because conditions did not increase by condition, and pulse repetition frequency, ensemble, and thresholds had the greatest impact on the twinkling artifact image. This study is expected to help examiners select optimal conditions to effectively increase twinkle artifact by adjusting color Doppler settings.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• Korean Journal of Radiology
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• v.20 no.8
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.343-354
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• 2005

The goal of this paper is to compare classification performances and to find a better classifier based on the characteristics of data. The compared methods are CART with two ensemble algorithms, bagging or boosting and SVM. In the empirical study of twenty-eight data sets, we found that SVM has smaller error rate than the other methods in most of data sets. When comparing bagging, boosting and SVM based on the characteristics of data, SVM algorithm is suitable to the data with small numbers of observation and no missing values. On the other hand, boosting algorithm is suitable to the data with number of observation and bagging algorithm is suitable to the data with missing values.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.16.1-16.1
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• 2011

$TiO_2$ is a promising material for gas sensors. To achieve high sensitivities, the material should exhibit a large surface-to-volume ratio and possess the high accessibility of the gas molecules to the surface. Accordingly, a wide variety of porous $TiO_2$ nanomaterials synthesized by wet-chemical methods have been reported for gas sensor applications. Nonetheless, achieving the large-area uniformity and comparability with well-established semiconductor production processes of the methods is still challenging. An alternative method is soft-templating which utilizes nanostructured inorganic or organic materials as sacrificial templates for the preparation of porous materials. Fabrication of macroporous $TiO_2$ films and hollow $TiO_2$ tubes by soft-templating and their gas sensing applications have been reported recently. In these porous materials composed of assemblies of individual micro/nanostructures, the form of links or necks between individual micro/nanostructures is a critical factor to determine gas sensing properties of the material. However, a systematic study to clarify the role of links between individual micro/nanostructures in gas sensing properties of a porous metal oxide matrix is thoroughly lacking. In this work, we have demonstrated a fabrication method to prepare highly-ordered, embossed $TiO_2$ films composed of anatase $TiO_2$ hollow hemispheres via soft-templating using polystyrene beads. The form of links between hollow hemispheres could be controlled by $O_2$ plasma etching on the bead templates. This approach reveals the strong correlation of gas sensitivity with the form of the links. Our experimental results highlight that not only the surface-to-volume ratio of an ensemble material composed of individual micro/nanostructures but also the links between individual micro/nanostructures play a critical role in evaluating the sensing properties of the material. In addition to this general finding, the facileness, large-scale productivity, and compatability with semiconductor production process of the proposed fabrication method promise applications of the embossed $TiO_2$ films to high-quality sensors.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.161-170
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• 2013

The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.235-244
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• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.