• 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.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.28 no.1
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• pp.37-51
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• 2009

The nuclear power industry in the world has recognized the importance of integrating non-technical and team skills training with the technical training given to its control room operators to reduce human errors since the Three Mile Island and Chernobyl accidents. The Nuclear power plant (NPP) industry in Korea has been also making efforts to reduce the human errors which largely have contributed to 120 nuclear reactor trips from the year 2001 to 2006. The Crew Resource Management (CRM) training was one of the efforts to reduce the human errors in the nuclear power industry. The CRM was developed as a response to new insights into the causes of aircraft accidents which followed from the introduction of flight recorders and cockpit voice recorders into modern jet aircraft. The CRM first became widely used in the commercial airline industry, but military aviation, shipboard crews, medical and surgical teams, offshore oil crews, and other high-consequence, high-risk, time-critical industry teams soon followed. This study aims to develop a CRM training program that helps to improve plant performance by reducing the number of reactor trips caused by the operators' errors in Korean NPP. The program is; firstly, based on the work we conducted to develop a human factors training from the applications to the Nuclear Power Plant; secondly, based on a number of guidelines from the current practicable literature; thirdly, focused on team skills, such as leadership, situational awareness, teamwork, and communication, which have been widely known to be critical for improving the operational performance and reducing human errors in Korean NPPs; lastly, similar to the event-based training approach that many researchers have applied in other domains: aircraft, medical operations, railroads, and offshore oilrigs. We conducted an experiment to test effectiveness of the CRM training program in a condition of simulated control room also. We found that the program made the operators' attitudes and behaviors be improved positively from the experimental results. The more implications of the finding were discussed further in detail.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.179-190
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• 2009

Travel time is one of the most important traffic parameters to evaluate operational performance of freeways. A variety of methods have been proposed to estimate travel times. One feasible solution to estimating travel times is to utilize existing loop detector-based infrastructure since the loops are the most widely deployed detection system in the world. This study proposed a new approach to estimate travel times for freeways. Inductive vehicle signatures extracted from the loop detectors were used to match vehicles from upstream and downstream stations. Ground-truthing was also conducted to systematically evaluate the performance of the proposed algorithm by recognizing individual vehicles captured by video cameras placed at upstream and downstream detection stations. A lexicographic optimization method vehicle reidentification algorithm was developed. Vehicle features representing the characteristics of individual vehicles such as vehicle length and interpolations extracted from the signature were used as inputs of the algorithm. Parameters associated with the signature matching algorithm were calibrated in terms of maximizing correct matching rates. It is expected that the algorithm would be a useful method to estimate freeway link travel times.

• Information Systems Review
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• v.19 no.4
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• pp.1-25
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• 2017

Despite high uncertainty on financial return, firms have continuously increased their investment on technologies because they recognize the potential value of technology investment in terms of enhancing operational efficiency and sustaining competitive advantage. Notably, an individual technology investment pattern or strategy within an industry may ultimately lead to significant differences in business performance. Hence, we first categorized technology investment into traditional research and development investment and information technology investment. Afterward, we examined the effects of each pattern with combination of the two types of technology investment on business performance according to firm size and position in the supply chain through fuzzy-set qualitative comparative analysis. Data collected from 562 manufacturing firms in Korea were used in the analysis. Results showed that large-sized firms were slightly affected with microscopic patterns in their technology investments, whereas small firms were highly affected with their technology investment patterns and their positions in the supply chain. The findings implied that a small enterprise requires an appropriate technology investment strategy to achieve successful business outcomes.

• Information Systems Review
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• v.15 no.3
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• pp.39-69
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• 2013

Business value of information technology has been the biggest interest of all such as practitioners and scholars for decades. Information technology is considered as the driving force or success factor of firm agility. The general assumption is that organizations making considerable efforts in IT investment are more agile than the organizations that are not. However, IT that should help the strategies of the firm that can hinder business or impede agility of the firm occasionally. In other words, it is still unknown if IT helps the agility of the firm or bothers it. Therefore, we note that contrary aspects of IT such as promotion and hindrance of firm agility have been observed frequently and theorize the relationships between them. In other words, we propose a rationale that firms should need to develop superior firm-wide IT capability to manage IT resources successfully in order to realize agility. Thus, this paper theorizes two IT capabilities, including technical IT capability and managerial IT capability as key factors impacting firm agility and firm performance. Further, we operationalize firm agility into two sub-types, including operational adjustment agility and market capitalizing agility. The data from 171 firms was analyzed using PLS approach. The results showed that technical IT capability has positive impact on firm agility and managerial IT capability had positive moderating effects between technical IT capability and firm agility. In addition, it was identified that top management championship positively moderates between agility and firm performance. Finally, it was indicated that firm agility was a very important causal variable of firm performance. Our study provides more exquisite and practical empirical evidences in the relationship between IT capability and firm agility by proposing applicable solution although IT has some contradicting effects on firm agility. Our findings suggest many useful implications to agility related researches in relatively primitive stage and working level officers in organizations.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.43-59
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• 2019

Today, with the spread of smartphones and the development of social networking services, structured and unstructured big data have stored exponentially. If we analyze them well, we will get useful information to be able to predict data for the future. Large amounts of data need to be collected first in order to analyze big data. The web is repository where these data are most stored. However, because the data size is large, there are also many data that have information that is not needed as much as there are data that have useful information. This has made it important to collect data efficiently, where data with unnecessary information is filtered and only collected data with useful information. Web crawlers cannot download all pages due to some constraints such as network bandwidth, operational time, and data storage. This is why we should avoid visiting many pages that are not relevant to what we want and download only important pages as soon as possible. This paper seeks to help resolve the above issues. First, We introduce basic web-crawling algorithms. For each algorithm, the time-complexity and pros and cons are described, and compared and analyzed. Next, we introduce the state-of-the-art web crawling algorithms that have improved the shortcomings of the basic web crawling algorithms. In addition, recent research trends show that the web crawling algorithms with special purposes such as collecting sentiment words are actively studied. We will one of the introduce Sentiment-aware web crawling techniques that is a proactive web crawling technique as a study of web crawling algorithms with special purpose. The result showed that the larger the data are, the higher the performance is and the more space is saved.