• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.

• Asian-Australasian Journal of Animal Sciences
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• 제24권4호
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• pp.449-456
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• 2011

Eighteen different biometric traits in 407 Kankrej cows from their breeding zone, i.e. Palanpur district of Gujarat, India, were recorded and analyzed by factor analysis to explain body conformation. The averages of body length, height at withers, height at shoulder, height at knee, heart girth, paunch girth, face length, face width, horn length, horn diameter, distance between horns, ear length, ear width, neck length, neck diameter, tail length with switch, tail length without switch and distance between hip bones were $123.44{\pm}0.37$, $124.49{\pm}0.28$, $94.68{\pm}0.30$, $38.2{\pm}0.14$, $162.56{\pm}0.56$, $178.95{\pm}0.70$, $44.09{\pm}0.10$, $15.91{\pm}0.05$, $42.47{\pm}0.53$, $26.07{\pm}0.19$, $13.34{\pm}0.08$, $31.24{\pm}0.12$, $16.10{\pm}0.05$, $50.63{\pm}0.18$, $73.21{\pm}0.32$, $111.62{\pm}0.53$, $89.34{\pm}0.34$ and $17.28{\pm}0.10\;cm$, respectively. The correlation coefficients between different traits ranged from -0.806 (horn diameter and distance between horns) to 0.815 (heart girth and paunch girth). Most of the correlations were positive and significant. Factor analysis with promax rotation with power 3 revealed three factors which explained about 66.02% of the total variation. Factor 1 described the cow body and explained 38.89% of total variation. The second factor described the front view/face of the cow and explained 19.68% of total variation. The third factor described the back of the cow and explained 7.44% of total variation. It was necessary to include some more variables for factor 3 to obtain a reliable estimate of the back view of the cow. The lower communities shown for distance between horns, horn diameter, ear width and neck diameter indicated that these traits did not contribute effectively to explaining body conformation and can be dropped from recording, whereas all other traits are important and needed to explain body conformation in Kankrej cows. The result suggests that principal component analysis (PCA) could be used in breeding programs with a drastic reduction in the number of biometric traits to be recorded to explain body conformation.

• Journal of Biosystems Engineering
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• 제29권4호
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• Asian-Australasian Journal of Animal Sciences
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• 제18권6호
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• pp.868-872
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• 2005

Individual management of the animal is the first step towards reaching the goal of precision livestock farming that aids animal welfare. Accurate recognition of each individual animal is important for precise management. Electronic identification of cattle, usually referred to as RFID (Radio Frequency Identification), has many advantages for farm management. In practice, however, RFID implementations can cause several problems. Reading speed and distance must be optimized for specific applications. Image processing is more effective than RFID for the development of precision farming system in livestock. Therefore, the aim of this paper is to attempt the identification of cattle by using image processing. The majority of the research on the identification of cattle by using image processing has been for the black-and-white patterns of the Holstein. But, native Japanese and Korean cattle do not have a consistent pattern on the body, so that identification by pattern is impossible. This research aims to identify to Japanese black cattle, which does not have a black-white pattern on the body, by using image processing and a neural network algorithm. 12 Japanese black cattle were tested. Values of input parameter were calculated by using the face image values of 12 cows. The face was identified by the associate neural memory algorithm, and the algorithm was verified by the transformed face image, for example, of brightness, distortion, noise and angle. As a result, there was difference due to a transformation ratio of the brightness, distortion, noise, and angle. The algorithm could identify 100% in the range from -30 to +30 degrees of brightness, -20 to +40 degrees of distortion, 0 to 60% of noise and -20 to +30 degree of angle transformed images.

• 한국항만경제학회지
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• 제31권4호
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• pp.75-89
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• 2015

본 연구에서는 각 항만들이 다루고 있는 31개의 수출입화물품목에 대하여 품목간 항만간 경쟁을 규명하고, 실제 항만간 전이량을 파악한 후, 가장 큰 폭의 물동량 하락을 경험하고 있는 항만의 향후 정책에 대한 제언을 하는 것을 연구 목적으로 하였다. 2005년~2014년 서해안 권역의 물동량 집중도는 점차 분산화되고 있는 것으로 나타났다. 특히 2009년을 기점으로 급격히 분산화가 진행되었으며 2014년에는 0.448으로 나타나 권역별 경쟁이 치열해지고 있었다. BCG Matrix를 이용해 인천항, 평택 당진항, 군산항의 정적포지셔닝과 동적포지셔닝을 분석한 결과, 정적포지셔닝분석에서는 인천항은 3사분면(Cash Cows),평택 당진항은 2사분면(Question Marks), 군산항은 (Dogs)군에 위치하고 있는 것으로 나타나 인천항은 비록 성장률은 낮지만 상대 항만군에 대한 높은 점유율로써 그 위치를 유지하고 있었다. 그러나 동적포지셔닝 분석에서는 시간이 흐름에 따라 인천항의 시장점유율과 성장률은 하락하고 있는 것으로 나타났다. 전이할당 분석결과, 인천항과 군산항의 물동량은 평택항으로 전이되고 있으며, 인천항과 군산항은 잠재성장치 대비 절대성장치가 평택 당진항보다 크게 못 미치는 것으로 밝혀졌다. LQ지수 분석결과, 타 항만과 중복되지 않는 인천항의 특화품목은 육류, 어패류 갑각류 등, 유연탄, 원유 및 석유, 석유정제품, 플라스틱 고무 및 그 제품, 방직용 섬유 및 그 제품, 비철금속 및 그 제품, 전기기기 및 그 제품, 항공기, 선박 및 그 부품으로 10개 품목으로 분석되었다.