• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.171-177
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• 2019

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.357-360
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• 2019

We propose an efficient single convolutional neural network (CNN) for pedestrian detection on embedded GPUs. We first determine the optimal number of the convolutional layers and hyper-parameters for a lightweight CNN. Then, we employ a multi-scale approach to make the network robust to the sizes of the pedestrians in images. Experimental results demonstrate that the proposed algorithm is capable of real-time operation, while providing higher detection performance than conventional algorithms.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.86-90
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• 2018

Standards related to express the non-flatness of a wafer are reviewed and discussed, for example, bow, warp, and sori. Novel wafer warpage measurement method is proposed for 3D stacked IC application. The new way measures heat transfer from a heater to a wafer, which is a function of the contact area between these two surfaces and in turn, this contact area depends on the wafer warpage. Measurement options such as heating from room temperature and cooling from high temperature were experimentally examined. The heating method was found to be sensitive to environmental conditions. The cooling technique showed more robust and repeatable results and the further investigation for the optimal cooling condition is underway.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.463-471
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• 2019

The variable transformations are useful ways to guarantee the functional relationships in the model. However, the presence of outliers may undermine the accuracy of transformation. This paper deals with response transformations in the partial linear models under the existence of outliers. A new procedure for response transformation and outliers detection is proposed. The procedure uses a sequential method for identifying outliers and dynamic graphical methods for an appropriate transformation. The graphical tools make it possible to catch diagnostic information by monitoring the movement of points in the data. The procedure is illustrated with several examples. Examples show that visual clues regarding the optimal transformation, the fittness of the model and the outlyness of the observations can be checked from the series of plots.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.260-263
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• 2023

Considering the growth of blockchain technology, the research aims to transform the efficiency of recommending optimal coffee suppliers within the complex supply chain network. This transformation relies on the extraction of vital transactional data and insights from stakeholders, facilitated by the dynamic interaction between the application interface (e.g., Rest API) and the blockchain network. These extracted data are then subjected to advanced data processing techniques and harnessed through machine learning methodologies to establish a robust recommendation system. This innovative approach seeks to empower users with informed decision-making abilities, thereby enhancing operational efficiency in identifying the most suitable coffee supplier for each customer. Furthermore, the research employs data visualization techniques to illustrate intricate clustering patterns generated by the K-Means algorithm, providing a visual dimension to the study's evaluation.

• Journal of Electrical Engineering and information Science
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• v.1 no.1
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• pp.29-38
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• 1996

To improve the reliability of control systems, certain robustness to plant uncertainties and disturbance inputs is required in terms of well founded mathematical basis. Robust control theory was set up and developed until now from this motivation. In this field, H$_2$or H\ulcorner norm performance measures are frequently used nowadays. Moreover a mixed H$_2$/H\ulcorner control problem is introduced to combine the merits of each measure since H$_2$control usually makes more sense for performance while H\ulcorner control is better for robustness to plant perturbations. However only some partial analytic solutions are developed to this problem under certain special cases at this time. In this paper, the mixed H$_2$/H\ulcorner control problem is considered. The analytic(or semi-analytic) solutions of (sub)optimal mixed H$_2$/H\ulcorner state-feedback controller are derived for the scalar plant case and the multivariable plant case, respectively. An illustrative example is given to compare the proposed analytic solution with the existing numerical one.

• Current Optics and Photonics
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• v.7 no.6
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• pp.708-713
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• 2023

A robust all-fiber nonlinear amplifying loop-mirror-based mode-locked femtosecond laser is demonstrated. Power-dependent nonlinear phase shift in a Sagnac loop enables stable and power-efficient mode-locking working as an artificial saturable absorber. The pump power is adjusted to achieve the lowest intensity noise for stable long-term operation. The minimum pump power for mode-locking is 180 mW, and the optimal pump power is 300 mW. The lowest integrated root-mean-square relative intensity noise of a free-running mode-locked laser is 0.009% [integration bandwidth: 1 Hz-10 MHz]. The long-term repetition-rate instability of a free-running mode-locked laser is 10^-7 over 1,000 s averaging time. The repetition-rate phase noise scaled at 10-GHz carrier is -122 dBc/Hz at 10 kHz Fourier frequency. The demonstrated method can be applied as a seed source in high-precision real-time mid-infrared molecular spectroscopy.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• International Journal of Heart Failure
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• v.6 no.3
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• pp.107-116
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• 2024

Frailty is highly prevalent among patients with heart failure (HF) and independently predicts adverse outcomes. However, optimal frailty definitions, assessments, and management in HF remain unclear. Frailty is common in HF, affecting up to 80% of patients depending on population characteristics. Even pre-frailty doubles mortality risk versus robust patients. Frailty worsens HF prognosis through systemic inflammation, neurohormonal changes, sarcopenia, and micronutrient deficiency. Simple screening tools like gait speed and grip strength predict outcomes but lack HF-specificity. Comprehensive geriatric assessment is ideal but not always feasible. Exercise, nutrition, poly-pharmacy management, and multidisciplinary care models can help stablize frailty components and improve patient-centred outcomes. Frailty frequently coexists with and exacerbates HF. Routine frailty screening should guide supportive interventions to optimize physical, cognitive, and psychosocial health. Further research on HF-specific frailty assessment tools and interventions is warranted to reduce this dual burden.

• Transactions of Materials Processing
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• v.33 no.5
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• pp.354-362
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• 2024

In this paper, the capabilities of recurrent neural networks (RNNs) to describe the viscoelastic properties of acrylonitrile-butadiene styrene (ABS) are investigated. The RNN model was trained using one-dimensional strains and corresponding stress data generated by the finite element method. The optimal model was then employed to predict the viscoelastic behavior of unseen test data. Furthermore, the viscoelastic-based RNN model was tested for extrapolation using other types of strain and corresponding stress data beyond the training set. The agreement between the predicted and actual stresses demonstrates the robust performance of the trained RNN model in predicting different types of strain inputs for larger strain tests, despite being trained only with step strain inputs. Therefore, the use of RNNs can be considered a viable alternative to conventional models for predicting viscoelastic behavior.