• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.1027-1037
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• 2021

This study aims to evaluate the performance of an electric multi-purpose cultivator through a simulation analysis. The simulation model was developed using commercial software, Simulation X, by applying the specifications of certain parts, such as an electric motor, a battery, and so on. The input parameter of the simulation was the engine load data according to the rotary tillage level using a conventional multi-purpose cultivator. The data were collected by configuring a load measurement system, and the load cycle was developed by repeating the data collection process under the most severe conditions. The average output engine torque values of conventional multi-purpose cultivator were 10.7, 13.0, 9.4, and 11.2 Nm in the D1P1, D1P2, D2P1, and D2P2 conditions, respectively. As a result of the simulation, the maximum values of the motor torque, rotational speed, and power of the electric multi-purpose cultivator were 16.8 Nm, 2,033.3 rpm, and 3.3 kW, respectively, and the motor was driven in sections within 70, 68, and 45% of the maximum output range. The rate of decrease of the battery state of charge (SOC) level per minute was approximately 0.6%, and it was possible to supply electric power to the motor for 9,550 sec. In the future study, research to verify and improve simulation models of electric multi-purpose cultivators should be conducted.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.168-168
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• 2020

This study is to evaluate the drought-related bigdata characteristics published from South Korean by developing crawler. The 5 years (2013 ~ 2017) drought-related posted articles were collected from Korean internet search engine 'NAVER' which contains 13 main and 81 local daily newspapers. During the 5 years period, total 40,219 news articles including 'drought' word were found using crawler. To filter the homonyms liken drought to soccer goal drought in sports, money drought economics, and policy drought in politics often used in South Korea, the quality control was processed and 47.8 % articles were filtered. After, the 20,999 (52.2 %) drought news articles of this study were classified into four categories of water deficit (WD), water security and support (WSS), economic damage and impact (EDI), and environmental and sanitation impact (ESI) with 27, 15, 13, and 18 drought-related keywords in each category. The WD, WSS, EDI, and ESI occupied 41.4 %, 34.5 %, 14.8 %, and 9.3 % respectively. The drought articles were mostly posted in June 2015 and June 2017 with 22.7 % (15,097) and 15.9 % (10,619) respectively. The drought news articles were spatiotemporally compared with SPI (Standardized Precipitation Index) and RDI (Reservoir Drought Index) were calculated. They were classified into administration boundaries of 8 main cities and 9 provinces in South Korea because the drought response works based on local government unit. The space-time clustering between news articles (WD, WSS, EDI, and ESI) and indices (SPI and RDI) were tried how much they have correlation each other. The spatiotemporal clusters detection was applied using SaTScan software (Kulldorff, 2015). The retrospective and prospective cluster analyses were conducted for past and present time to understand how much they are intensive in clusters. The news articles of WD, WSS and EDI had strong clusters in provinces, and ESI in cities.

• Annual Conference of KIPS
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• 2021.11a
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• pp.942-943
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• 2021

The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.

• Annual Conference of KIPS
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• 2021.11a
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• pp.391-392
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• 2021

해마다 실내 냉방기 냉매누설 문제가 고질적으로 반복되며 소비자들의 피해도 커져가고 있다. 특히 제조사와 설치 업체가 다른 경우 냉매 누수의 원인이 제품인지, 설치하자인지 책임소재를 두고 갈등을 빚는 경우가 빈번하다. 이에 더 이상 소비자들의 피해를 막기 위해 냉매누설 검출 방안 마련이 필요해 보인다. 본 연구에서는 실내 냉방기 설치 후 냉매누설 검출을 위한 별도의 하드웨어 장치 추가 없이 냉방기의 운영을 위해 설치된 센서들의 값을 이용하여 냉매누설의 유무를 판단할 수 있는 방안을 제안하는 것을 목적으로 한다. 데이터 분석을 위하여 제조사의 제품 출하 전 현장 테스트 단계에서 측정한 온도값, 전류값, 습도값을 취합하여 데이터 셋을 구축하였다. 이때 자동화된 머신러닝(AutoML)을 이용하여 데이터의 80%를 훈련 데이터로 20%를 테스트 데이터로 사용하여 냉매량 80%는 1, 그 이하는 0으로 훈련시켰다. 구축한 데이터 셋을 이용하여 훈련시킨 결과 99% 정확도로 냉매누설 검출을 분별할 수 있었다. 또한 냉매누설과 관련성이 높은 중요 특징 4개를 추출할 수 있었다. 본 연구를 통하여 별도의 하드웨어 장치 추가 없이 소프트웨어적인 접근 방법으로 문제를 해결할 수 있는 feasibility를 확인할 수 있었다.

• Convergence Security Journal
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• v.22 no.1
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• pp.61-69
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• 2022

When a large-scale cyber attack or terrorism occurs and the country suffers enormous damage or poses a fatal threat to security, social interest in nurturing cybersecurity workforce increases. In addition, the government often suggests policies and guideline to train cybersecurity workforce. However, the system that can systematically manage trained cyber workforce after they are employed in related organizations or companies is still weak. Software workforce has a standardized qualification level model, so appropriate jobs are set and managed for each level. Cyber workforce also need a specialized qualification level model that takes into account their career, academic background, and education&training performance. By assigning a qualification level, the duties that can be performed for each level should be set, and the position and duty of the department should also be assigned in consideration of the level. Therefore, in this paper, we propose a qualification level model for cyber security workforce.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.663-668
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• 2023

This paper describes a mobile application that can recognize and identify cats residing on a university campus using the Google's machine learning platform, 'Teachable Machine'. Machine learning, one of the core technologies of the Fourth Industrial Revolution, performs an efficient task of finding optimal results through data learning. Therefore, the model is learned and generated using the platform based on machine learning, and then implemented as an application for smartphones, so that cats can be identified simply and efficiently. In this application, if you take a picture of a cat directly on the spot or call it from the gallery, the cat is identified and information about the cat is provided. Though this system was developed for a specific university campus, it is expected that it can be extended to other campuses and other species of animals.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.199-220
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• 2023

Research competitiveness is the future of the university and the future of the country. This study is to present considerations when university libraries introduce and perform research performance analysis services and to propose guidelines for selection of analysis solutions. In order to achieve the purpose of this study, 6 university library staff were interviewed and 4 research performance analysis solutions were compared. The contents of the interview include the background of service promotion, service content, subscription solution, service provision scope and provision method. The research performance analysis solutions selected for analysis are InCite, SciVal, RIMS and Scholytics. As a result of analyzing the four solutions, it was found that most of the software has the necessary functions to perform research performance analysis service tasks, and generally subscribed to the solution according to the purpose of analysis. The results of this study can be used as reference materials for performing research performance analysis services in university libraries.

• Journal of Hydrogen and New Energy
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• v.33 no.2
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• pp.148-157
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• 2022

Research on hydrogen storage is active to properly deal with hydrogen, which is considered a next-generation energy medium. In particular, research on metal hydride with excellent safety and energy efficiency has attracted attention, and among them, magnesium-based hydrogen storage alloys have been studied for a long time due to their high storage density, low cost, and abundance. However, Mg-based alloys require high temperature conditions due to strong binding enthalpy, and have many difficulties due to slow hydrogenation kinetics and reduction in hydrogen storage capacity due to oxidation, and various strategies have been proposed for this. This research manufactured Mg₂Ni to improve hydrogenation kinetics and synthesize about 5, 10, 20 wt% of CaF₂ as a catalyst for controlling oxidation. Mg₂NiHx-CaF₂ produced by hydrogen induced mechanical alloying analyzed hydrogenation kinetics through an automatic PCT measurement system under conditions of 423 K, 523 K, and 623 K. In addition, material life cycle assessment was conducted through Gabi software and CML 2001 and Eco-Indicator 99' methodology, and the environmental impact characteristics of the manufacturing process of the composites were analyzed. In conclusion, it was found that the effects of resource depletion (ARD) and fossil fuels had a higher burden than other impact categories.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.335-343
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• 2019

Healthy living is an essential element of human happiness. Quality eating provides the basis for life, and the health of livestock, which provides meat and dairy products, has a direct impact on human health. In the case of calves, diarrhea is the cause of all diseases.In this paper, we use a sensor to measure calf 's biometric data to diagnose calf diarrhea. The collected biometric data is subjected to a preprocessing process for use as meaningful information. We measure calf birth history and calf biometrics. The ontology is constructed by inputting environmental information of housing and biochemistry, immunity, and measurement information of human body for disease management. We will build a knowledge base for predicting calf diarrhea by predicting calf diarrhea through logical reasoning. Predict diarrhea with the knowledge base on the name of the disease, cause, timing and symptoms of livestock diseases. These knowledge bases can be expressed as domain ontologies for parent ontology and prediction, and as a result, treatment and prevention methods can be suggested.