• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.211-218
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• 2004

In a large owner organization, a program manager of multiple maintenance and remodeling projects has experienced increasing scale and complexity of coordinating the M/R projects with in-house technicians who belong to multiple trade shops. This paper proposes a dual-level hierarchical planning strategy that consists of a program master plan in the long-term horizon and a master construction schedule in an operational scheduling window. A rolling horizon approach to the program master plan is proposed to deal with the external uncertainty of unknown stream of project requests. A resource-constrained scheduling algorithm is developed to generate the master construction schedule in a scheduling window. During development of the algorithm, more emphasis is placed on long-term organizational resource continuity, especially flow management of program constraint resources, than ephemeral events of an individual activity and project. Monte Carlo simulation experiments of three scheduling windows are used to evaluate the relative performance of the proposed scheduling algorithm against three popular scheduling heuristics for resource-constrained multiple projects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1962-1969
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• 2009

During past 20 years we have witnessed brilliant advances in major components of computer system, including CPU, memory, network device and HDD. Among these components, in spite of its tremendous advance in capacity, the HDD is the most performance dragging device until now and there is little affirmative forecasting that this problem will be resolved in the near future. We present a new approach to solve this problem using the NAND Flash memory. Researches utilizing Flash memory as storage medium are abundant these days, but almost all of them are targeted to mobile or embedded devices. Our research aims to develop the NAND Flash memory based storage system enough even for enterprise level server systems. This paper present structural and operational mechanism to overcome the weaknesses of existing NAND Flash memory based storage system, and its evaluation.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.380-395
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• 2019

Seakeeping, especially for the roll motions, is of critical importance to the safe operation of fishing boats in Indonesia. In this study, a traditional East Java Fishing Boat (EJFB) has been analysed in terms of its seakeeping performance. Furthermore, a bilge keel was designed to reduce the roll motions of the EJFB using multiple stages approach. After installing the designed bilge keels, it was shown that up to 11.78% and 4.87% reduction in the roll response of irregular seaways and the total resistance under the design speed, respectively. It was concluded that the roll-stabilized-EJFB will enhance the well-being of the fisherman and contribute to the boats' safe operation, especially in extreme weather conditions. Moreover, the total resistance reduction of the EJFB due to the installation of the designed bilge keels also resulted in increased operational efficiency and reduced fuel costs and fuel emissions for local stakeholders.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of Space Technology and Applications
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• v.3 no.3
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• pp.229-238
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• 2023

One of the important ways to improve the performance of magnetometers in satellite exploration is to reduce magnetic noise from satellites. One of the methods to decrease magnetic noise is by extending the satellite boom. However, this approach is often not preferred due to its high cost and operational considerations. Therefore, in many cases, removing interference from the satellite platform in the measured dataset is widely utilized after data acquisition. In this study, we would like to introduce an algorithm for removing magnetic noise observed from magnetometers installed on two solar panels and one main body without a boom.

• Korea Trade Review
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• v.48 no.3
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• pp.151-176
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• 2023

This paper critically examines the role performance of local distributors within the Stimulus-Organism-Response (SOR) model, while also considering the moderating influence of market competition on the organism (O) and response (R) elements. Adopting a holistic approach, the SOR model provides a comprehensive framework for analyzing how external stimuli, including distributive, procedural, and interaction unfairness, interact with internal psychological processes, such as perceived unfairness, to shape the long-term orientation of importing agents. Moreover, this study acknowledges the pivotal role of market competition in the operational context of local distributors. It posits that competitive market dynamics play a crucial role in intensifying the relationship between behavioral factors and the long-term orientation of distributors, thereby revealing contingent effects within the SOR model. Through the exploration of these dynamics, this study contributes to a comprehensive understanding of the interplay among external stimuli, internal psychological processes, and market competition within the SOR framework, advancing our knowledge in this field.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.112-116
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• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.1-7
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• 2024

This paper presents the design and implementation of a VPN (Virtual Private Network) module to address the need for external access and functional expansion of NVR (Network Video Recorder) systems. NVR systems play a critical role in enhancing security across various industries through real-time monitoring and recording. However, they are vulnerable to security threats, particularly when a secure connection to external networks is required. To resolve this issue, this study applied a VPN module to ensure that NVR systems can communicate securely with external networks. This approach enabled remote access and real-time event notifications. Performance tests confirmed 100% accuracy in event notifications. This research contributes to improving the security and operational efficiency of NVR systems, highlighting the necessity and utility of VPN modules for secure communication with external networks.

• Fire Science and Engineering
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• v.34 no.2
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• pp.110-119
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• 2020

The National Fire Safety Code (NFSC) sets forth the installation methods and technical standards of firefighting facilities. This information is stipulated in the attached Table 1 of the Enforcement Decree of the Act on Fire Prevention and Installation, Maintenance and Safety Control of Fire-Fighting Systems. The NFSC serves as a foundation for fire prevention and public safety. However, the current version of the NFSC has been under scrutiny due to its delayed enactment and revision process. This is because of its structural inflexibility, time-consuming procedures, and mixed usage of both performance and technical standards. Furthermore, there are difficulties with keeping its unique specialties due to the absence of a specialized, permanent independent entity that enacts, revises, and maintains its standards. Moreover, the NFSC lacks collectivity, openness, and consistency. Therefore, to overcome the aforementioned obstacles, this study investigates the operational and legal status of the NFSC and the problems regarding its enactment and revision process. Further, it presents suggestions for system improvement by analyzing and comparing the information with domestic and foreign counterparts dedicated to managing their similar technical NFSC standards. First, the study recommends that the legal performance and technical standards mixed within the current NFSC should be separated. Second, the enactment and revision of technical standards should be implemented by the private sector and not by the government. Third, technical standards should adopt a user-oriented approach for the code system.