• International conference on construction engineering and project management
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• 2013.01a
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• pp.403-411
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• 2013

Horizontal construction projects such as oil and gas pipeline projects typically involve repetitive-work activities with the same crew and equipment from one end of the project to the other. Repetitive scheduling also known as linear scheduling is known to have superior schedule management capabilities specifically for such horizontal construction projects. This study discusses on expanding the capabilities of repetitive scheduling to account for the variance in production rates and visual representation by developing an automated alignment based linear scheduling program for applying temporal and spatial changes in production rates. The study outlines a framework to apply changes in productions rates when and where they will occur along the horizontal alignment of the project and illustrates the complexity of construction through the time-location chart through a new linear scheduling model, Linear Scheduling Model with Varying Production Rates (LSM_VPR). The program uses empirically derived production rate equations with appropriate variables as an input at the appropriate time and location based on actual 750 mile natural gas liquids pipeline project starting in Wyoming and terminating in the center of Kansas. The study showed that the changes in production rates due to time and location resulted in a close approximation of the actual progress of work as compared to the planned progress and can be modeled for use in predicting future linear construction projects. LSM_VPR allows the scheduler to develop schedule durations based on minimal project information. The model also allows the scheduler to analyze the impact of various routes or start dates for construction and the corresponding impact on the schedule. In addition, the graphical format lets the construction team to visualize the obstacles in the project when and where they occur due to a new feature called the Activity Performance Index (API). This index is used to shade the linear scheduling chart by time and location with the variation in color indicating the variance in predicted production rate from the desired production rate.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.635-642
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• 2023

With the development of artificial intelligence technology, drones are evolving beyond simple remote control tools into intelligent drones that perform missions autonomously. The importance of drones is gradually gaining attention due to the use of drones in overseas military conflicts and the analysis of the future operational environment in Korea. AMAD is proposed for the rapid development of intelligent drones. In order to develop intelligent software based on AMAD, an integrated development environment (IDE) that supports users with functions such as debugging, performance evaluation, and monitoring is essential. In this paper, we define the concepts of the development environment required for intelligent software development and describe the results of reflecting them in the design of the IDE and AMAD's agents, SVI and MPD, which are interfaced with the IDE.

• Journal of the Korean Society of Physical Medicine
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• v.18 no.4
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• pp.109-119
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• 2023

PURPOSE: The definition and scope of biofeedback are broad and lack a clear framework. Therefore, efforts are needed to clearly understand the exact range and definition of biofeedback based on the research and development conducted to date. Thus, the purpose of this study was to arrive at the definition and scope of biofeedback through a literature review and analysis of its application methods. METHODS: This study is a systematic literature review conducted to understand the various types and effects of biofeedback. International databases such as Google Scholar and PubMed were used. Domestic databases utilized for keyword searches included the Research Information Sharing Service (RISS) and the National Digital Science Library (NDSL). Quality assessment of the selected studies in the selection process was done using the Cochrane risk of bias, and the research was analyzed according to the population, intervention, control, and outcomes (PICO) format. RESULTS: Studies conducted between 2019 and 2021 were selected, with 4 papers falling under physiological classifications and 7 under biomechanical classifications. The quality assessment results showed that random sequence generation, allocation concealment, performance bias, and reporting bias were unclear. Detection bias was moderate, and attrition bias and other biases were low. Out of the 11 papers, 9 dealt with physical function outcomes, 5 with daily life activities, and 3 with mental functions. CONCLUSION: Physiological biofeedback tended to influence psychological factors more than physical functions, while biomechanical biofeedback tended to have a positive impact on physical functions.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.239-253
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• 2024

This research applies both point-wise and pair-wise learning strategies within the learning-to-rank (LTR) framework to predict horse race rankings in Seoul. Specifically, for point-wise learning, we employ a linear model and random forest. In contrast, for pair-wise learning, we utilize tools such as RankNet, and LambdaMART (XGBoost Ranker, LightGBM Ranker, and CatBoost Ranker). Furthermore, to enhance predictions, race records are standardized based on race distance, and we integrate various datasets, including race information, jockey information, horse training records, and trainer information. Our results empirically demonstrate that pair-wise learning approaches that can reflect the order information between items generally outperform point-wise learning approaches. Notably, CatBoost Ranker is the top performer. Through Shapley value analysis, we identified that the important variables for CatBoost Ranker include the performance of a horse, its previous race records, the count of its starting trainings, the total number of starting trainings, and the instances of disease diagnoses for the horse.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.85-96
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• 2024

As users of paid media readers' satisfaction with usage directly affects the promotion and development of paid media in China. Based on relevant research literature on user sustained use behavior, using the Expectation Confirmation Model as the framework, China paid media Caixin is used as a content product to construct a sustained use model for paid media. At the same time, the operational definition and theoretical assumptions of the variables in the model were provided, laying the foundation for subsequent empirical research on the effectiveness of the model.The research results show that paid media's social influence and performance expectations have a positive impact on Caixin App readers' adoption behavior,perceived usefulness and expection confirmation has a positive impact on Caixin App readers' satisfaction . Adoption behavior and satisfaction has a positive effect on continue using intention, what's more,the perceived usefulness also has a positive effect on continue using intention.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.1-8
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• 2024

This paper employs stochastic processing techniques to analyze explosion risks in plant facilities based on explosion return periods. Release probability is calculated using data from the Health and Safety Executive (HSE), along with annual leakage frequency per plant provided by DNV. Ignition probability, derived from various researchers' findings, is then considered to calculate the explosion return period based on the release quantity. The explosion risk is assessed by examining the volume, radius, and blast load of the vapor cloud, taking into account the calculated explosion return period. The reference distance for the design blast load model is determined by comparing and analyzing the vapor cloud radius according to the return period, historical vapor cloud explosion cases, and blast-resistant design guidelines. Utilizing the multi-energy method, the blast load range corresponding to the explosion return period is presented. The proposed return period serves as a standard for the design blast load model, established through a comparative analysis of vapor cloud explosion cases and blast-resistant design guidelines. The outcomes of this study contribute to the development of a performance-based blast-resistant design framework for plant facilities.

• Steel and Composite Structures
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• v.51 no.1
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• pp.25-41
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• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.

• Convergence Security Journal
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• v.24 no.1
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• pp.35-42
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• 2024

Multi-tenancy architecture plays a crucial role in cloud-based services and applications, and data isolation within such environments has emerged as a significant security challenge. This paper investigates various data isolation methods including schema-based isolation, logical isolation, and physical isolation, and compares their respective advantages and disadvantages. It evaluates the practical application and effectiveness of these data isolation methods, proposing security considerations and selection criteria for data isolation in the development of multi-tenant websites. This paper offers important guidance for developers, architects, and system administrators aiming to enhance data security in multi-tenancy environments. It suggests a foundational framework for the design and implementation of efficient and secure multi-tenant websites. Additionally, it provides insights into how the choice of data isolation methods impacts system performance, scalability, maintenance ease, and overall security, exploring ways to improve the security and stability of multi-tenant systems.