• The Journal of Information Systems
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• v.31 no.1
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• pp.25-58
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• 2022

Purpose The purpose of this study is to analyzes the RPA fitting processes by the casese of manufacturing SMEs(Small and Medium-sized Enterprises) in an exploraty approach. Based on the findings on the RPA fitting processes, we intend to provide a cornerstone for developing a general-purpose RPA introduction model in the future. Design/methodology/approach In this study, empirical cases of RPA fitting processes were analyzed based on interviews with project managers of specialized IT suppliers in charge of RPA development and managers of IT departments of manufacturing SMEs that actually introduced RPA. In order to explore various RPA fitting process in the manufacturing value chain, a total of 7 manufacturing SMEs were interviewed, ranging from companies using a legacy system to companies without a legacy system. Over the primary and secondary activity processes, the details of RPA processes were analyzed in the steps of 'Frequency Identification, Input Processing, Source Identification, Inquiry and Processing, Information Registration, Result Reporting'. Findings From the analysis, we derived some exploratory results that the processes over 0.25 FTE and related with many suppliers and clients are fitting for RPA introduction in manufacturing SMEs Our results will provide basic data for the development of the future general-purpose RPA introduction model for manufacturing SMEs, providing practical reference for RPA introduction.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.149-160
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• 2022

The article considers the peculiarities of developing a marketing strategy for the adaptation of small businesses to quarantine restrictions in the field of commercial entrepreneurship. The importance of reformatting the existing marketing strategy in connection with the change of key conditions of trade activity with the introduction of quarantine restrictions due to the covid19 virus epidemic is substantiated. Quarantine restrictions and the temporary introduction of lockdown in various countries around the world, including Ukraine, have not only caused a crisis for small businesses. But they became a shock therapy and accelerated the digitalization of retail. Trends in digitalization and development of digital infrastructure allow both to adapt the structures of commercial entrepreneurship to the current conditions, and set directions for development in the long run. Particular attention in the article is paid to changing the business model and automation of sales processes based on the introduction of vending. The preconditions and existing experience of vending in Ukraine are analyzed. An outline of the business model of the project for the sale of goods through vending machines has been developed.

• Fourth Industrial Review
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• v.3 no.1
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• pp.1-12
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• 2023

Purpose - In Korea, where 64% of the land is forested, mountaineering is a leisure activity enjoyed by the majority of the people. As new technologies named the 4th industrial revolution spread more after the Covid-19 pandemic, we propose a human and technology convergence curriculum for mountaineering and trekking education to enjoy safety in the field of mountaineering and trekking using cutting-edge technology. Research design, data, and methodology - After examining the current state of the mountaineering industry and preceding studies on mountaineering and camping, and learning about BAC the 100 famous mountains, mountaineering gamification, and Gamification We designed an AI convergence curriculum using. Result - Understanding the topography and characteristics of mountains in Korea, acquiring mountaineering information through AI convergence, selecting mountaineering equipment suitable for the season, terrain, and weather, setting educational goals to safely climb, and deriving term project results. A total of 15 A curricula for teaching was proposed. Conclusion - Artificial intelligence technology is applied to the field of mountaineering and trekking and used as a tool, and it is expected that the base of mountaineering will be expanded through safe, efficient, fun, and sustainable education. Through this study, it is expected that the AI convergence education curriculum for mountaineering and trekking will be developed and advanced through several studies.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.760-767
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• 2022

This paper introduces alert fatigue and presents methods to measure alert fatigue by using physiological signals. Alert fatigue is a phenomenon that which an individual is constantly exposed to frequent alarms and becomes desensitized to them. Blind spots are one leading cause of struck-by accidents, which is one most common causes of fatal accidents on construction sites. To reduce such accidents, construction equipment is equipped with an alarm system. However, the frequent alarm is inevitable due to the dynamic nature of construction sites and the situation can lead to alert fatigue. This paper introduces alert fatigue and proposes methods to use physiological signals such as electroencephalography, electrodermal activity, and event-related potential for the measurement of alert fatigue. Specifically, this paper presents how raw data from the physiological sensors measuring such signals can be processed to measure alert fatigue. By comparing the processed physiological data to behavioral data, validity of the measurement is tested. Using preliminary experimental results, this paper validates that physiological signals can be useful to measure alert fatigue. The findings of this study can contribute to investigating alert fatigue, which will lead to lowering the struck-by accidents caused by blind spots.

• Journal of Practical Agriculture & Fisheries Research
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• v.9 no.1
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• pp.91-104
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• 2007

The special project was conducted at the cooperative farm where located at Yakjeon-ri Sukcheon-gun Pyeongannam-do, the Democratic People's Republic of Korea. This farm was firstly introduced a newly developed technology-"Bokto seeding technology" for rice cultivation from the Republic of Korea. Total acreage of rice paddy field cultivated by this technology was 800ha and the average yield was 7.17t/ha with paddy rice which was higher by 109.2% than that of the transplanting method for rice cultivation. In general rice disease was decreased at the Bokto seeded rice plant compared to the transplanted rice plant and root activity was higher in Bokto seeded rice. Optimum seeding amount was determined at rate of 90kg/ha in Pyeongdo 5(early ripening variety) and 110kg/ha at Pyeongdo 11(medium ripening variety) and Pyeongyang 43(late ripening variety), respectively. A recommended sowing time was within late April for late ripening variety like Pyeongyang 43, May 1-5 for medium ripening variety, and May 5-15 for early ripening variety.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1871-1877
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• 2023

The main goal of the coordinated project development of therapeutic radiopharmaceuticals of Y-90 and Re-188 is to exploit advancements in radionuclide production technology. Here, direct and indirect production methods with medium reactor and cyclotron are compared to evaluate derived neutron flux and production yield. First, nano-sized ¹⁸⁶W and ⁸⁹Y specimens are suspended in water in a quartz vial by FLUKA simulation. Then, the solution is irradiated for 4 days under 9E+14 n/cm2/s neutron flux of reactor. Also, a neutron activator including three layers-lead moderator, graphite reflector, and polyethylene absorbent- is simulated and tungsten target is irradiated by 60 MeV protons of cyclotron to generate induced neutrons for ¹⁸⁸W and ⁹⁰Sr production via neutron capture. As the neutron energy reduced, the flux gradually increased towards epithermal range to satisfy (n/2n,γ) reactions. The obtained specific activities at saturation were higher than the reported experimental values because the accumulated epithermal flux and nano-sized specimens influence the outcomes. The beta emitters, which are widely utilized in brachytherapy, appeal an alternative route to locally achieve a rational yield. Therefore, the proposed method via neutron activator may ascertain these broad requirements.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.505-509
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• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.387-396
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• 2020

Wearable biosensors have the potential to non-invasively and continuously monitor seniors' stress in their daily interaction with the urban environment, thereby enabling to address the stress and ultimately advance their outdoor mobility. However, current wearable biosensor-based stress detection methods have several drawbacks in field application due to their dependence on batch-learning algorithms. First, these methods train a single classifier, which might not account for multiple subjects' different physiological reactivity to stress. Second, they require a great deal of computational power to store and reuse all previous data for updating the signle classifier. To address this issue, we tested the feasibility of online multi-task learning (OMTL) algorithms to identify multiple seniors' stress from electrodermal activity (EDA) collected by a wristband-type biosensor in a daily trip setting. As a result, OMTL algorithms showed the higher test accuracy (75.7%, 76.2%, and 71.2%) than a batch-learning algorithm (64.8%). This finding demonstrates that the OMTL algorithms can strengthen the field applicability of the wearable biosensor-based stress detection, thereby contributing to better understanding the seniors' stress in the urban environment and ultimately advancing their mobility.