• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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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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• v.24 no.4
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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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• v.29 no.4
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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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• v.18 no.6
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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.

• Journal of Korea Port Economic Association
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• v.31 no.4
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• pp.75-89
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• 2015

This study examines 31 import and export cargo items handled in each port to investigate which items face the most competition among the ports and how many of them are transited to other ports. The study aims to suggest implications for the future port policy of Incheon Port. It was found that the volume concentration in the Western Coast region from 2005 to 2014 became increasingly decentralized. The decentralization began in earnest in 2009 in particular, and the value was 0.448 in 2014, indicating fierce competition among the regions. According to the static and dynamic positioning analyses results for Incheon Port, Pyeongtaek and Dangjin Port, and Gunsan Port, using BCG Matrix, the static positioning analysis showed that Incheon Port belongs to the 3rd quadrant (Cash Cows), Pyeongtaek and Dangjin Port belongs to the 2nd quadrant (Question Marks), and Gunsan Port belongs to the (Dogs) group. This implies that Incheon Port has maintained its position with large shares compared to those of other ports, despite its low growth rate. However, the market position and growth rate of Incheon Port decreased according to the dynamic positioning analysis results. The shift-share analysis results indicated that the volumes of Incheon Port and Gunsan Port were shifting to Pyeongtaek and Dangjin Port. Moreover, the ratio of absolute growth to potential growth of Incheon Port and Gunsan Port turned out to be significantly lower than that of Pyeongtaek and Dangjin Port, implying that Incheon Port and Gunsan Port are declining as compared to Pyeongtaek Port and Dangjin Port. According to the LQ index analysis results, specialized items from Incheon Port that do not overlap with other ports included the following ten items: meat, fish and crustaceans, bituminous coals, crude oil and petroleum, petroleum-refined products, plastic rubber and products, textiles, nonferrous metal and products, electric machinery, and aircrafts and ships. In particular, it was confirmed that the bulk cargo of Incheon Port was actually shifting to Pyeongtaek and Dangjin Port following the policy of re-establishing port functions.