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전 광명,박 도진,임 채준 조선대학교 IT연구소 2021 정보기술융합공학논문지 Vol.11 No.2
가축 질병은 농가의 비용 증가로 인한 소득 감소의 중대한 요인 중 하나이며, 특히 가축 전염병은 확산방지를 위하여 광범위한 살처분이 이루어지기에 그 피해가 더 심각하다. 전염병의 발생 원인으로는 축사 방역 시설이 열악하고 농가와 축산관련자의 방역 의식이 낮거나 농장 및 방역 현장에서 비용과 인력, 방역 의식 문제로 법을 지키지 않는 경우등을 원인으로 꼽을 수 있다. 가축 전염병의 사전 통제를 위해 본 논문에서는 가축 전염병 예측을 위한 지능형 센서 네트워크를 제안한다. 제안된 방법은 가축 농장 내에는 카메라와 환경 센서를 통해 입력받은 값을 인공지능 모델로 판단하여 관련 정보 및 도출된 정보를 컬럼 지향 데이터베이스에 저장하고 관리한다. 구체적으로 가축 전염병 발생과 높은상관성을 지니는 농장 간 차량 이동, 농장 소독, 급이 패턴, 동물 활동성, 그리고 야생동물 출몰의 5가지 관리 요소를실시간으로 모니터링하고 정보를 취합하기 위한 멀티모달 사물지능 센서간 네트워크를 구성한다. 제안된 방법의 실효성을 검증하기 위해서, 전라남도 소재 800두 규모의 모돈 번식 농장에 제안방법의 테스트베드를 구축하고 6개월간 운영하며 관리 요소별 측정 및 분석 데이터를 수집하였다. 테스트베드 운영결과, 가축 전염병 발생 예측확률이 운영 전28.33%에서 6.67% 수준으로 낮아진 것을 확인할 수 있었으며, 이는 가축 전염병 예방을 위한 주요 방역 지침들이 정량적으로 평가되고 관리될 수 있음을 보였다. Livestock disease is one of the major factors in the decrease in income due to the increase in the cost of farm households. In particular, livestock infectious diseases are more serious because of widespread culling to prevent the spread. The causes of infectious diseases include poor quarantine facilities in livestock pens, low awareness of quarantine among farms and livestock related people, and non-compliance with laws due to cost, manpower, and awareness of quarantine at farms and quarantine sites. For the proactive control of livestock epidemics, this paper proposes an intelligent sensor network for predicting livestock epidemics. The proposed method determines the input values through the camera and environmental sensors in the livestock farm as an artificial intelligence model, and stores and manages the related information and the derived information in a column-oriented database. Specifically, it monitors and collects information in real time by five management factors: vehicle movement between farms, farm disinfection, feeding patterns, animal activity, and wild animal appearance, which have a high correlation with the occurrence of livestock epidemics. In order to verify the effectiveness of the proposed method, a test bed of the proposed method was built on an 800-head sow breeding farm in Jeollanam-do, operated for 6 months, and measurement and analysis data for each management element were collected. As a result of the test bed operation, it was confirmed that the prediction probability of the occurrence of livestock infectious diseases was lowered from 28.33% before operation to 6.67%, which showed that major quarantine guidelines for the prevention of livestock infectious diseases can be quantitatively evaluated and managed.
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장대영,전 광명 한국방송∙미디어공학회 2024 방송공학회논문지 Vol.29 No.1
It is known that drones have many noise factors such as propeller noise and wind, making it almost impossible to acquire soundusing an installed microphone. However, if drone sound acquisition is possible, the sense of presence can be greatly improved byproviding real sound along with drone video. In this paper, we would like to introduce research trends and cases where thepossibility has been attempted to see if it is possible to overcome these difficulties. The application field aims to provide real-timerealistic media for remote tourism using drones, and for this purpose, it was decided to acquire stereo sound that provides abandwidth of 8 kHz or more. In order to obtain wide-band stereo sound, various methods including physical approach, microphoneelement approach, and signal processing approach were explored. Stereo directional microphones were mounted at a distance from thedrone propeller, noise proof filters and windscreens were used, and a DNN (Deep Neural Network) based real-time drone noisecancellation was applied. Although the characteristics of the drone noise recorded during actual drone flight were very complex andthe level was greater than expected, we confirmed the possibility of overcoming drone noise through various approaches andproviding sound service along with drone video. In fact, as a result of measuring the noise reduction performance of each approachseparately in the laboratory, a noise reduction effect of approximately 24 dB was confirmed, and through listening, the music sound,which was barely audible due to drone noise, was enhanced to a level where the music could be well perceived.
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