• Journal of Digital Convergence
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• v.19 no.10
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• pp.105-110
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• 2021

The pig ICT automatic feeder is capable of automatically feeding feed, etc. according to the set conditions. However, there is a disadvantage that the setting condition itself must depend on the user's experience. Therefore, trial and error is caused, and there is a problem that the efficiency is lowered. Therefore, it is necessary to develop a system and implement a service model that can improve pig productivity by suggesting optimal feeding setting conditions based on data. Therefore, in this paper, a pig feeding management service model was developed using the performance analysis program such as the existing feeding data, breeding management data, and pig production management system. Through this, we developed a consumer-oriented feed management service model that can be efficiently utilized by analyzing pig data. In addition, it is possible to provide a service that contributes to a decrease in the mortality rate and an increase in the MSY of the farms with the intelligent automatic feeding management service, thereby improving the productivity of the pig farms and thereby increasing the income of the pig farms.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.813-816
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• 2009

최근 정보통신 기술을 다양한 분야에 접목하여 융합 서비스를 제공하는 연구 및 개발이 진행되고 있다. 특히 농수산 분야에 RFID 기술을 이용하여 생산 이력 관리하거나 센서 네트워크를 적용하여 생산 환경의 데이터를 수집하고 있다. 본 논문에서는 습도, 온도, 이산화탄소, 조도 센서 등의 양돈 환경을 실시간 감시 응용을 개발하기 위해 지도 기반의 기본 시스템 구조, 데이터베이스 구조, 지도 및 센싱 데이터 도시 알고리즘 등을 제시한다. 이를 통해 각 양돈사에 설치된 센서 데이터를 지도 상에서 직접 현재의 양돈 환경이 상태를 확인하고, 각 시간대별, 센서별로 구분하여 실시간 센싱 데이터를 감시 할수 있다. 이 연구 결과는 돼지들이 성장할 수 있는 최적의 환경을 구축하고, 돼지의 생육에 투입되는 사료 등을 최소화하고 최적의 생육환경을 조성하여 생산성을 높이는 데 도움이 될 것으로 사료된다.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.31-40
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• 2020

As a result of reenforcement of livestock industry licensing and livestock related vehicle registration system, pig farm are mandatory for setting stop bar before pig farm entrance and maintaining the records of inlet and outlet to and from pig farm. Also, the first step to the pig farm biosecurity is the control of entrance human, vehicles, animals and the related records including entrance and sterilization. In this study, the pig farm entrance control system using RFID technology and web-server system was developed. For human entrance, contact type RFID reading system(13.56MHz) was developed. And the 900MHz RFID system was used to detect vehicle entrance. The test result shows that system actuating, recognition, saving records to the web server was successful. Then, the full system of human entrance recording unit, vehicle entrance recording unit, entrance control system, human sterilizing booth containing a tablet for inputting visitor's record and sterilization record was tested. The records were wrote to web server DB through the data management web-program. Performance test shows the entrance control and data management in server was successfully operated.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1162-1165
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• 2012

어미로부터 생후 21일령(또는 28일령)에 젖을 때는 어린 자돈들은 면역력이 약하여 통상 폐사율이 30~40%까지 치솟는 등 자돈 관리가 국내 양돈 농가의 가장 큰 문제 중 하나로 인식되고 있다. 본 논문에서는 이러한 양돈 농가의 문제를 해결하기 위하여 자돈사(새끼돼지 축사)에 카메라와 마이크를 설치하고 획득된 영상과 소리 정보를 이용하여 자돈들을 모니터링하는 시스템을 제안한다. 제안된 시스템은 실시간으로 유입되는 영상과 소리 스트림 데이터로부터 각각 움직임 벡터와 평균 피치 값을 추출하여 이미 설정된 정상 상황의 임계치 값을 넘는 순간부터를 불특정 이상 상황이라 판단한다. 실제, 경상남도 함양군의 한 돼지 농장에 A/V 센서 기반의 실험 환경을 구축하고 2012년 6월 한 달간의 이유자돈 돈사의 모니터링 데이터 셋을 취득하였고 전반기 15일간의 데이터 셋을 이용하여 자돈사 모니터링 시스템의 프로토타입을 설계 구현하였으며 후반기 15일간의 A/V 스트림 데이터로는 검증 실험을 수행하였다.

• Korea Information Processing Society Review
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• v.29 no.3
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• pp.22-29
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• 2022

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.183-185
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• 2015

Recently, big data analysis based on machine learning has gained popularity and many machine learning techniques have been applied to the field of agriculture. By using machine learning technique to analyze huge number of samples of biological and environmental data, new observations can be found. In this research, we consider the estimation of proportional daily weight increase (PDWI) based on measurement data from experimental swine farm. In order to derive the exact formulation for PDWI estimation, we have used measured value of mean, daily maximum, daily minimum of temperature, humidity, CO2, wind speed and measured PDWI values. Based on collected data, we have derived equation for PDWI estimation using tree-based algorithm. In the derived formulation, we have found that the daily average temperature is the most dominant factor that affects PDWI. Our results can be applied to pig farms to estimate the PDWI of swine.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.33-40
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• 2024

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.223-225
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• 2013

어미로부터 생후 21일령 또는 28일령에 젖을 때는 어린 자돈들은 면역력이 약하여 통상 폐사율이 30~40%까지 치솟는 등 자돈 관리가 국내 양돈 농가의 가장 큰 문제 중 하나로 인식되고 있다. 본 논문에서는 이러한 양돈 농가의 문제를 해결하기 위하여 자돈사에 비디오 카메라를 설치하고 획득된 영상 정보를 이용하여 자돈들을 관리하는 시스템을 제안한다. 특히 제안된 시스템은 실시간으로 유입되는 영상 스트림 데이터로부터 움직임 여부를 신속히 판단하고, 움직임이 없는 경우(수면 또는 휴식)에 바닥면적 중 자돈들이 차지하지 않은 부분의 면적을 추출하여 수면 또는 휴식 중 자돈들의 밀집 여부를 판단한다. 즉, 카메라를 이용하여 과도하게 밀집된 경우 온도를 올려주고 반대의 경우 온도를 낮춰주는 온도제어 시스템을 설계할 수 있다. 실제, 경상남도 함양군의 한 돼지 농장에 비디오 센서 기반의 실험 환경을 구축하고 자돈사 감시 데이터 셋을 취득하였고, 이를 이용하여 제안된 자돈사 관리 시스템의 프로토타입을 개발하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2B
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• pp.252-262
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• 2010

Recently, the USN (Ubiquitous Sensor Network) technology is emerging as an aspect of digital convergence trends which is being rapidly evolving in the whole society. The technological feasibility for the various application services using the USN is researched in numerous industries, but, in the agricultural field, the market of USN application service, technology adoption and commercialization have been delayed. In the agricultural field, the ubiquitous technologies could lead to huge change in the conventional surroundings such as growth environment of livestock, crop cultivation and harvest. In this paper, to offer a integrated management, we construct a u-swinery(ubiquitous swinery) system which is consisted with USN environmental sensors to collect information from physical phenomenon such as luminance, relative humidity, temperature and ammonia gas. Numbers of CCTV were also installed to monitor inside and outside of the swinery. The u-swinery integrated management system can monitor and control the condition of swinery from remote sites. Furthermore, by gathering the cumulative environmental data from the system, the optimal growth condition for the livestock could be created.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2348-2354
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• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.