• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.49-63
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• 2024

Objective : The purpose of this study was to investigate parental perspectives on cognitive rehabilitation using a combination of phenomenological research methodology and word cloud analysis. Methods : Interviews were conducted with five parents of children with developmental disabilities. Word cloud analysis was conducted using Python, and five researchers analyzed the meaning units and themes using phenomenological methods. Words with high frequency were considered as a heuristic tool. Results : A total of 43 meaning units and nine components related to the phenomenon of cognitive rehabilitation were derived, and three themes were finalized. The main themes encompassed the definition of cognitive rehabilitation, challenges associated with cognitive rehabilitation, and factors influencing the selection of a cognitive rehabilitation institute. Cognitive rehabilitation emerged as a treatment focused on improving learning, daily functioning, and cognitive abilities in children with developmental disabilities. The perceived issues with cognitive rehabilitation pertained to treatment methods, therapist expertise, and associated costs. In addition, parents highlighted the importance of therapist expertise, humane personality, and affordability of cost and schedule when choosing a cognitive rehabilitation institute. Conclusion : Parents expressed expectations for substantial improvements in their children's daily functioning through cognitive rehabilitation. However, challenges were identified in clinical practices. Going forward, we expect that cognitive rehabilitation will evolve into a better therapeutic support service addressing the concerns raised by parents.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.298-305
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• 2023

Burkitt's lymphoma is a distinct subtype of non-Hodgkin's lymphoma originating from B-cells that is notorious for its aggressive growth and association with immune system impairments, potentially resulting in rapid and fatal outcomes if not addressed promptly. Optimizing the use of Food and Drug Administration-approved medications, such as combining known safe drugs, can lead to time and cost savings. This method holds promise in accelerating the progress of novel treatments, ultimately facilitating swifter access for patients. This study explores the potential of a dual-targeted therapeutic strategy, combining the bruton tyrosine kinase-targeting drug Ibrutinib and the epidermal growth factor receptor/human epidermal growth factor receptor-2-targeting drug Lapatinib. Ramos and Daudi cell lines, well-established models of Burkitt's lymphoma, were used to examine the impact of this combination therapy. The combination of Ibrutinib and Lapatinib inhibited cell proliferation more than using each drug individually. A combination treatment induced apoptosis and caused cell cycle arrest at the S and G2/M phases. This approach is multifaceted in its benefits. It enhances the efficiency of the drug development timeline and maximizes the utility of currently available resources, ensuring a more streamlined and resource-effective research process.

• Information Systems Review
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• v.20 no.1
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• pp.137-158
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• 2018

Online platforms recently expanded their connectivity through an authing service. The growth of authing services enabled consumers to enjoy easy log in access without exerting extra effort. However, multiple points of access increases the security vulnerability of platform ecosystems. Despite the importance of balancing authing service and security, only a few studies examined platform connectivity. This study examines the optimal level of authing service of a platform and how authing strategies impact participants in a platform ecosystem. We used a game-theoretic approach to analyze security problems associated with authing services provided by online platforms for consumers and other linked platforms. The main findings are as follows: 1) the decreased expected loss of consumers will increase the number of players who participate in the platform; 2) linked platforms offer strong benefits from consumers involved in an authing service; 3) the main platform will increase its effort level, which includes security cost and checking of linked platform's security if the expected loss of the consumers is low. Our study contributes to the literature on the relationship between technology convenience and security risk and provides guidelines on authing strategies to platform managers.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.43-48
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• 2024

The discovery of a two-dimensional electron gas (2DEG) at the interface of LaAlO₃ (LAO) and SrTiO₃ (STO) substrates has sparked significant interest, providing a foundation for cutting-edge research in electronic devices based on complex oxide heterostructures. However, conventional methods for producing LAO thin films, typically employing techniques like pulsed laser deposition (PLD) within physical vapor deposition (PVD), are associated with high costs and challenges in precisely controlling the La and Al composition within LAO. In this study, we adopted a cost-effective alternative approach-solution-based processing-to fabricate LAO thin films and investigated their electrical properties. By adjusting the concentration of the precursor solution, we varied the thickness of LAO films from 2 to 65 nm and determined the sheet resistance and carrier density for each thickness. After vacuum annealing, the sheet resistance of the conductive channel ranged from 0.015 to 0.020 Ω·s^-1, indicating that electron conduction occurs not only at the LAO/STO interface but also into the STO bulk region, consistent with previous studies. These findings demonstrate the successful formation and control of 2DEG through solution-based processing, offering the potential to reduce process costs and broaden the scope of applications in electronic device manufacturing.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• Membrane Journal
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• v.34 no.3
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• pp.192-203
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• 2024

The advancement of commercially viable gas separation membranes plays a pivotal role in improving CO₂ separation efficiency. High-molecular-weight poly(ethylene oxide) (high-Mw PEO) emerges as a promising option due to its high CO₂ solubility, affordability, and robust mechanical attributes. However, the crystalline nature of high-Mw PEO hinders its application in gas separation membranes. This study proposes a straightforward blending approach by incorporating various polymeric additives into high-Mw PEO to address this challenge. Four commercially available, water-soluble polymers, i.e. poly(ethylene glycol) (PEG), poly(propylene glycol) (PPG), poly(acrylic acid) (PAA), and poly(vinyl pyrrolidone) (PVP) are examined as additives to enhance membrane performance by improving miscibility and reducing PEO crystallinity. Contrary to expectations, PEG and PPG fail to inhibit the crystalline structure of PEO and result in membrane flaws. Conversely, PAA and PVP demonstrate greater success in altering the crystal structure of PEO, yielding defect-free membranes. A thorough investigation delves into the correlation between changes in the crystalline structure of high-Mw PEO blend membranes and their gas separation performance. Drawing from our findings and previously documented outcomes, we offer insights into designing and selecting additive polymers for high-Mw PEO, aiming at the creation of cost-effective, commercially viable CO₂ separation membranes.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.131-140
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• 2024

Aids to navigation, such as buoys used in maritime environments, play a crucial role in providing accurate information to navigating vessels, enabling them to precisely determine their position and maintain safe routes by marking surrounding hazardous areas. However, collisions between ships and these aids result in substantial costs for buoy damage and repair. While high-end equipment is currently used to prevent such accidents, its widespread adoption is hindered by cost concerns. This paper presents research on a maritime object detection algorithm utilizing embedded systems to address this issue. Previous studies employed the Hough transform for horizon detection, but its high computational demands posed challenges for real-time processing. To overcome this limitation, our approach first performs image segmentation, followed by an optimized Otsu algorithm for horizon detection. Subsequently, we establish a Region of Interest (ROI) based on the detected horizon, focusing on areas with a high risk of ship collision. Within this ROI, particularly below the horizon line, maritime objects are detected. A Convolutional Neural Network (CNN) model is then applied to determine whether the detected objects are ships. Objects classified as ships within the ROI are considered potential collision risks.