• Journal of Digital Convergence
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• v.20 no.5
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• pp.411-422
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• 2022

The purpose of this study was to identify factors that affect the characteristics and intentions of the elderly to use digital wealth management services. The subjects of this study were 312 elderly people over 50 years old. Based on the Value-based Adoption Model(VAM), the research model added price value, social influence, and perceived risk as research variables. As a result of empirical analysis, it was found that usefulness, enjoyment, price value, and social influence all had a significant positive (+) effect on perceived value. It was found that technicality had a significant negative (-) effect. On the other hand, no significant effect relationship was tested on perceived risk. The perceived value had a significant positive (+) effect on the intention to use. This study was meaningful in the academic research that it applied a research model that reflected the characteristics of the elderly who were not treated as mainstream in the technology acceptance model for digital wealth management services. In addition, it provided practical implications for providers' marketing strategies and government/public institution policy establishment to increase the use of digital wealth management services for the elderly.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.305-305
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• 2023

수문조사기기 검정은 강수량, 수위, 유량 등과 같은 수문자료를 관측하는 수문조사기기가 대상지역의 수문상황을 정확하게 관측하는지를 검사하는 일련의 과정으로 「수자원의 조사 계획 및 관리에 관한 법률」 제12조에 법적 기반을 두고 있다. 검정 대상은 강수량, 수위, 유속, 유사량, 토양수분량, 증발산량, 증발량 측정기기 총 7종이며, 환경부장관으로부터 한강홍수통제소가 검정업무를 위임받고, 한국건설기술연구원과 한국수자원조사기술원이 위탁받아 운영중에 있다. 최근에는 증발산량, 토양수분량 및 유량 측정기기기 등이 첨단화되어 기존 검정 방식에 대한보완 및 신설에 대한 요구가 증가하고 있다. 특히, 유량 측정시 기존에 사용하였던 회전식 유속계는 ADCP(Acoustic Doppler Current Profiler) 유량측정기기로 대체되어 활용률이 2013년 24%에서2021년 67%로 약 2.8배 급격히 증가하였다. 하지만 수문조사기기 검정 관련 고시 내 ADCP에 대한검사방법 및 허용오차 등의 규정이 부재하여 수문조사기기의 검정 공백이 발생하는 등의 문제가 존재하고 있다. 이에 본 연구에서는 ADCP 운영 및 기술 현황, 현행 법령, 국외 사례 등을 검토하여 ADCP 유량측정기기의 검사방법 및 허용오차에 대한 방안을 제시하고자 한다. ADCP 검사방법은 총 5단계로 외관검사, 자가진단 검사, 온도센서 검사, 수심측정 검사, 유량비교측정 검사에 따라 검정을 실시한다. 첫 번째 외관검사시에는 기기 외관과 센서 등 물리적 손상을 점검하고, 두 번째 자가진단 검사에서는 센서 변환 매트릭스 값, 수신부 센서 테스트, RAM/ROM 테스트, 통신 테스트 등에 관한 정상값 산출 여부를 확인한다. 세 번째 온도센서 검사에서는 검증용 온도센서를 이용한 값과 ADCP에 부착된 온도센서 값과 차이를 확인하고 ±2℃초과시 재검사 또는 적절한 조치를 취한 후 다음 단계의 검사를 진행한다. 네 번째 수심측정 검사에서는 수조 내 수심 측정을 확인하여 실제 수심과의 오차를 확인하고 ±1% 초과시 재검사 또는 적절한 조치 후 다음 검사를 실시한다. 유량비교 측정검사에서는 각 기기 간의 평균유량의 상대오차를 평가하는 것으로 ±5%미만에는 합격, ±5이상 ±10%미만에서는 재검사, ±10%이상에서는 공장수리를 권고하도록 하고, 1~5 단계의 검사를 통과한 기기를 대상으로 인증서를 발급하도록 한다. 유량비교 측정검사시에는 매년 ADCP를 사용하는 일반기업 및 공공기관 등이 모여 ADCP의 성능을 상호간 비교하는 'ADCP 기술협력 워크숍'을 확장하여 실시할 수 있다. 각 검사 단계의 허용오차는 USGS 또는 제조사 기준과 2022년 ADCP 기술협력 워크숍 성능검사 분석 결과를 토대로 하였다. 본 ADCP 검정 방안은 향후 ADCP 모델별로 단계별 시범 검토를 통해 세부사항에 대한 제시가 필요하며, 온도센서 검사, 수심측정 검사, 유량 비교측정검사에 대한 허용오차에 대한 타당성에대한 검증 및 검토가 이루어져야 할 것으로 사료된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.377-380
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• 2022

An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.335-340
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• 2021

This study conducted research on creating an environment for maker education programs for robot and SW education, development and application of maker education programs for low-grade elementary school students in farming and fishing villages. Based on the preceding maker education model, the OMCSI model was developed for the lower grade level of elementary school, and based on this, five WeDo-utilized elementary maker education programs were developed. From April 1, 2020 to October 30, 2020, the results of applying the elementary school maker education program using WeDo Robot 2.0 to 10 second graders of 10 Elementary School in Gyeongsangnam-do are as follows. The average increased by 3.40 points (t=-2.378, p=0.034) and the average increased by 3.30 points (t=-2.329, p=0.040). The average was also increased by 3.40 points (t=-2.458, p=0.038). Finally, it rose to 3.70 points (t=-2.449, p=0.037) for its reasoning ability. That is, all four sub-elements of computing thinking had a significant probability of 0.04, indicating statistical significant differences between scores of pre-post computing thinking. Therefore, the Elementary Maker Education Program using WeDo robots has worked very effectively to improve students' computing thinking skills.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.575-576
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• 2020

본 논문은 근래에 식용곤충 식품에 대한 수요 및 국민적 관심이 증가하여 관련 산업이 급격히 성장하고 있는 가운데, 건강기능성 효과가 널리 알려진 흰점박이꽃무지 유충의 안정적인 생산량 확보를 위한 스마트 사육사를 제작하고 그 성능을 평가한 결과이다. 사육사는 L6m×W3m×H2.8m 크기로 제작하였으며, 안정적인 사육환경을 위하여 사육실과 공조실을 분리하여 설계하였다. 공시재료는 생후 15일이 경과된 흰점박이꽃 무지 유충 1령이며, 스마트 사육사 내 사육환경은 온도 25±2℃, 습도 65±5%로 제어하였다. 사육조사는 매주 1회, 유충의 체중, 길이, 두께를 측정하였으며, 스마트 사육사의 성능평가를 위해 일반 사육농가(전북 소재)와 비교·분석하였다. 사육 4주 후 조사 결과, 스마트 사육사에서 사육한 유충의 체중과 길이는 각각 평균 1.97g/마리와 3.75cm로, 일반농가의 1.58g/마리와 3.55cm에 비해 비교적 높은 것으로 나타났다. 하지만, 두께의 경우 2주 차까지 일반농가에서 대체로 높은 것으로 나타났으며, 이후 3~4주 차에서는 큰 차이를 보이지 않았다. 따라서 본 연구를 통해 개발한 흰점박이꽃무지 유충 스마트 사육사는 일반농가와 비교해 사육이 비교적 빠르고 생산량을 더 많이 확보할 수 있는 시스템으로 농가소득 증대에 유용할 것으로 판단되며, 장소 및 시간에 상관없이 생육환경 제어가 가능하여 개발된 시제품의 보급 확대가 필요하다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.15-17
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• 2022

In the aftermath of the avian influenza that occurred from the second half of 2020 to the beginning of 2021, 17.8 million laying hens were slaughtered. Although the government invested more than 100 billion won for egg imports as a measure to stabilize prices, the effort was not that easy. The sharp volatility of egg prices negatively affected both consumers and poultry farmers, so measures were needed to stabilize egg prices. To this end, the egg prices were successfully predicted in this study by using the analysis algorithm of a machine learning regression. For price prediction, a total of 8 independent variables, including monthly broiler chicken production statistics for 2012-2021 of the Korean Poultry Association and the slaughter performance of the national statistics portal (kosis), have been selected to be used. The Root Mean Square Error (RMSE), which indicates the difference between the predicted price and the actual price, is at the level of 103 (won), which can be interpreted as explaining the egg prices relatively well predicted. Accurate prediction of egg prices lead to flexible adjustment of egg production weeks for laying hens, which can help decision-making about stocking of laying hens. This result is expected to help secure egg price stability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.122-124
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• 2022

Handling Passenger Traffic is the main challenge for designing an elevator group-control algorithm. Advanced control systems such as Hyundai's Destination Selection System(DSS) lets passengers select the destination by pressing on a selecting screen, and the systems have shown great efficiency. However, the algorithm cannot be applied to the general elevator control system due to the expensive cost of the technology. Often many elevator systems use Nearest Car(NC) algorithms based on the SCAN algorithm, which results in time efficiency problems. In this paper, we designed an elevator group-control algorithm for specific buildings that have approximate timetable data for most of the passengers in the building. In that way, it is possible to predict the destination and the location of passenger calls. The algorithm consists of two parts; the waiting function and the assignment function. They evaluate elevators' actions with respect to the calls and the overall situation. 10 different timetables are created in reference to a real timetable following midday traffic and interfloor traffic. The specific coefficients in the function are set by going through the genetic algorithm process that represents the best algorithm. As result, the average waiting time has shortened by a noticeable amount and the efficiency was close to the known DSS result. Finally, we analyzed the algorithm by evaluating the meaning of each coefficient result from the genetic algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.360-363
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• 2022

According to the 2018 Kaggle ML & DS Survey, among the proportions of frameworks for machine learning and data science, TensorFlow and Keras each account for 41.82%. It was found to be 34.09%, and in the case of development programming, it is confirmed that about 82% use Python. A significant number of machine learning and deep learning structures utilize the Keras framework and Python, but in the case of Python, distribution and execution are limited to the Python script environment due to the script language, so it is judged that it is difficult to operate in various environments. This paper implemented a machine learning and deep learning system using C# and Keras running in Visual Studio 2019. Using the Mnist dataset, 100 tests were performed in Python 3.8,2 and C# .NET 5.0 environments, and the minimum time for Python was 1.86 seconds, the maximum time was 2.38 seconds, and the average time was 1.98 seconds. Time 1.78 seconds, maximum time 2.11 seconds, average time 1.85 seconds, total time 37.02 seconds. As a result of the experiment, the performance of C# improved by about 6% compared to Python, and it is expected that the utilization will be high because executable files can be extracted.

• Knowledge Management Research
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• v.24 no.4
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• pp.219-235
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• 2023

The advancement of digital technology and the impact of COVID-19 have brought about changes in corporate innovation and organizational culture, thereby highlighting the significance of Smart Learning in the field of HRD (Human Resource Development). This trend has led to an increased interest in personalized Smart Learning among employees due to the growth of hybrid work and the widespread adoption of smart work practices. This study aimed to illuminate the relative importance of the factors that constitute Smart Learning from the perspective of HRD practitioners. Through a review of prior literature, Smart Learning hierarchy and factors most fitting to the current context were identified, and their relative importance was determined using the AHP method. Consequently, in the first-tier factors, importance was confirmed in the order of 'Learning Activities', 'Teaching Activities', 'Learning Content', 'Assessment and Evaluations', and 'Learning Time and Space'. At the second-tier encompassing all factors, 'Pedagogical Strategy', 'Learning Results', 'Learning Tasks', 'Learning Goal', and 'Learning Support' emerged within the top five factors. These findings are significant in that they redefine the concept of smart learning and propose an academic framework for future research. Additionally, from a practical perspective, it is anticipated that this study will contribute valuable insights for HRD practitioners, aiding them in focusing on which factors to prioritize for enhancing and advancing Smart Learning initiatives.