• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.49-57
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• 2022

Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.263-264
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• 2013

기존의 도서관 정보서비스는 도서관 업무담당자에 의한 1:n 방식의 온라인 지식서비스만을 강조하였다면 스마트 도서관시스템에서는 빅데이터를 통해 지식을 생성, 검증, 분류하여 지능형지식, 실감형지식, 맞춤형지식, 체험형지식 등을 제공할 수 있다. 또한 빅데이터를 활용한 다자간 콘텐츠 공유, 상호 의견 교환이 가능하며, 집단지성에 의해 구축되는 학습 콘텐츠 및 지식 베이스는 국가의 지식자원 경쟁력을 향상시킬 수 있으며, 차세대 이러닝 환경에서의 지능형 튜터링을 통해 창의적 인재육성, 공교육의 질적 향상, 사교육비 절감, 교육 기회 균등 배분, 지역 및 계층 간 위화감 해소 등 국가정책 목표 실현할 수 있다. 제안된 빅데이터 기반의 스마트도서관 정보서비스시스템에서는 멀티테넌트 환경에서 구현이 가능한 핵심요소들을 개발하였다. 그러므로 초기 투자비용이 거의 없고, 쉽고, 간편하며, 저비용 IT 서비스가 가능한 SaaS 기반의 소프트웨어 온-디멘드 방식의 서비스 모델로 시스템을 구현하였다. 또한 연결방식으로는 N고객:1인스턴스, 제공 프로그램은 동일한 코드 사용, 커스터마이징은 고객이 테넌트별 환경 설정을 통해서 직접 수정가능, 데이터는 테넌트별 자료를 공유해서 사용할 수 있으며 기존의 디지털도서관 시스템 서비스의 단점을 해결할 수 있도록 성능을 개선하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.505-507
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• 2021

Image recognition technology is a technology that recognizes an image object by using the generated feature descriptor and generates object feature points and feature descriptors that can compensate for the shape of the object to be recognized based on artificial intelligence technology, environmental changes around the object, and the deterioration of recognition ability by object rotation. The purpose of the present invention is to implement a power management framework required to increase profits and minimize damage to livestock farmers by preventing accidents that may occur due to the improvement of efficiency of the use of livestock house power and overloading of electricity by integrating and managing a power fire management device installed for analyzing a complex environment of power consumption and fire occurrence in a smart safety livestock house, and to develop and disseminate a safe and optimized intelligent smart safety livestock house.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.9-18
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• 2013

In 2014, third and fourth graders in primary schools will start using digital textbooks for social science, science, and English. Fifth and sixth graders will follow the next year. Given this situation, this study investigated the awareness of digital textbooks by primary school teachers who did not have direct experience with digital textbooks. Also studied was the teachers' acceptance of digital textbooks, based on the Technology Acceptance Model. The results showed that most respondents were not ready to use digital textbooks, and they were apprehensive about their use. However, if the teachers were required to use digital textbooks, usefulness and playfulness were key factors in their acceptance.

• Smart Media Journal
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• v.12 no.1
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• pp.24-31
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• 2023

Meta-learning is metacognition that instantly distinguishes between knowing and unknown. It is a learning method that adapts and solves new problems by self-learning with a small amount of data.A few-shot learning method is a type of meta-learning method that accurately predicts query data even with a very small support set. In this study, we propose a method to solve the limitations of the prototype created with the mean-point vector of each class. For this purpose, we use the few-shot learning method that created the prototype used in the few-shot learning method as the median prototype. For quantitative evaluation, a handwriting recognition dataset and mini-Imagenet dataset were used and compared with the existing method. Through the experimental results, it was confirmed that the performance was improved compared to the existing method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.348-349
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• 2017

본 논문에서 제안하는 내용은 기존의 포털 검색의 키워드 입력 방식과는 달리, 검색하고자 하는 대상을 스마트폰과 같은 모바일 기기의 카메라로 촬영하면, 해당 촬영 이미지가 사용자 입장에서는 검색 키워드와 같이 동일한 역할을 할 수 있도록 이미지에 해당되는 검색 키워드를 추출 및 매칭하여 검색을 위한 질의어로 사용할 수 있도록 해주는 것을 목적으로 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.441-442
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• 2021

본 논문에서는 고해상도의 시계열 위성영상을 딥러닝 알고리즘으로 학습하여 도시 변화탐지를 수행한다. 고해상도 위성영상을 활용한 서비스는 4 차 산업혁명 융합 신사업 중 하나인 스마트시티에 적용하여 도시 노후화, 교통 혼잡, 범죄 등 다양한 도시 문제 해결 및 효율적인 도시를 구축하는데 활용이 가능하다. 이에 본 연구에서는 도시 변화탐지를 위한 딥러닝 알고리즘으로 DeepLabV3+를 사용한다. 이는 인코더-디코더 구조로, 공간 정보를 점진적으로 회복함으로써 더욱 정확한 물체의 경계면을 찾을 수 있다. 제안하는 방법은 DeepLabV3+의 레이어와 loss function 을 수정하여 기존보다 좋은 결과를 얻었다. 객관적인 성능평가를 위해, 공개된 데이터셋 LEVIR-CD 으로 학습한 결과로 평균 IoU 는 0.87, 평균 Dice 는 0.93 을 얻었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.33-35
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• 2020

이전 연구에 존재했던 영상에서 이미지를 추출하여 학습 데이터로 사용 할 때 시계열성을 고려하지 않은 상태에서의 검증은 정확하지 않을 수 있음을 설명한다. 정확한 형체를 가진 물체의 경우 매 프레임 마다 일정한 모양을 유지할 가능성이 크지만, 기체나 액체처럼 유동성이 큰 형태를 분사 혹은 방류 할 때 순간적인 간섭 혹은 분산에 의해 실제 값이 분사 량 혹은 방류량과 다를 수 있다. 본 연구에서는 이전 연구 중 Yolov3와 youtube 영상을 이용하여 연기 형태를 추출하고, 이를 Resnet에 학습시킨 연구를 이용하여 이와 비슷한 사례의 연구에서 나타날 수 있는 검증 오류들을 설명한다.

• Journal of IKEEE
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• v.22 no.1
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• pp.53-60
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• 2018

Smart farm dissemination by continuously developing IoT is one of the best solution for decreasing labor in Korea farming area because of ageing. For this reason, the number of Smart farm in Korea is being increased. The Smart farm can control farming environment such as temperature for human. Specially, The important thing is controlling proper temperature for farming. In order to control the temperature, legacy smart farms are usually using pans or air conditioners which can control the temperature. However, those devices result in increasing production cost because the electric power consumption is high. For this reason, we propose a smart farm which can predict the proper temperature after an hour by using Deep learning to minimize the electric power consumption by controlling window instead of pans or air conditioners. We can see the 83% of electric power saving by means of the proposed smart farm.