• Journal of the Korea Organic Resources Recycling Association
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• v.25 no.2
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• pp.77-90
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• 2017

In order to promote recycling of animal and plant residues, it is necessary to prepare detailed statistics on the sources, generation amount and the state of disposal so that waste recycling companies and enterprises can obtain the information easily. Also, the recycling methods specified in the law should be appropriate. For this, the study reviewed the appropriateness of detailed classification of animal and plant residues and permitted recycling methods in the Enforcement Regulations of the Waste Management Act of Korea. For improvement of the detailed classification, the study conducted literature review on European and Japanese ones. Additionally, we visited slaughterhouses of livestock and poultry, vegetable oils manufacturing companies, starches and glucose or maltose manufacturing companies, which generate the waste and recycle the waste, to grasp the status of recycling in Korea. Based on the results, the study proposes improvement measures for the detailed classification and the permitted recycling types in the law.

• Journal of Animal Science and Technology
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• v.49 no.3
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• pp.405-414
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• 2007

Pigs have been considered as an ideal source of donor organs because of their plentiful supply and their numerous anatomical and physiological similarities to the human in xenotransplantation. However, for the public health risks associated with the potential for porcine endogenous retrovirus(PERV) infection through xenograft from pig to human, the investigation of methods for elimination and/or control of PERV has been required. In this study we developed the detection and classification methods for PERV based on PCR using specific primers. PERV-A and PERV-B were found in all pigs including Berkshire, Duroc, Landrace, Yorkshire, miniature pig, and Korean native black pig from Jeju by PCR with type-specific primers for PERV. However, PERV-C was detected only from Duroc, miniature pig, and Korean native black pig from Jeju. PERV-A and PERV-B could be distinguished by PCR-RFLP with BamHI. These methods for PERV will be useful in rapid screening of safe organ for xenograft, furthermore, helpful in monitoring of PERV during and after xenotransplantation.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of Animal Environmental Science
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• v.7 no.1
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• pp.45-56
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• 2001

The following statistics are the results of a survey that analyzed the classification of livestock area and spatial mobility based upon the number of livestock and an area of 151 towns and cities from 1975 to 1995. 1. As a results of analysis about the degree of location concentration using C.V., Korean native cattles (HanWoo) and swines are becoming more centralized while dairies and chickens are becoming decentralized. 2. 49 regions, that is 32.5%, were classified as growing regions, 30 regions (19.9%) were stagnant regions and 72 regions (47.7%) were withering regions. The classification was based upon the calculation according to the numbers of converted grown animals and growth index. Kyonggi-do and Chungchongnam-do, specifically, took up 26.6% and 24.5% of the developing regions which shows that these two regions are the dominant regions for livestock. 3. Kyongsangbuk-do and Chungchongnam-do play significant roles for overall livestock, and Chollanam-do is considering a transition from swines to Korean native cattles and Kyongsangbuk-do is shifting from Korean native cattles to swines.

• Journal of Animal Environmental Science
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• v.5 no.3
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• pp.139-148
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• 1999

This study was conducted to develop an image processing system which can classify the pig's thermoregulatory behavior under the different environmental conditions. The 4 pigs of 25kg were housed in the environmentally controlled chamber(1.4m$\times$2.2m floor space). Postural behavior of the pigs was captured with an CCD color camera. The raw behavioral images were processed by thresholoding, reduction, separation of slightly contacted pigs, labeling, noise removal, computation of number of labels, and classification of the pig's behavior. The correct classification rate of the image processing system was 97.8%(88 out of 90 testing images). The results of this study showed that the image processing system could be used for a behavior-based automatic environmental controller.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.151-152
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• 2019

In this paper, we propose an improved learning method based on a small data set for animal image classification. First, CNN creates a training model for a small data set and uses the data set to expand the data set of the training set Second, a bottleneck of a small data set is extracted using a pre-trained network for a large data set such as VGG16 and stored in two NumPy files as a new training data set and a test data set, finally, learn the fully connected network as a new data set.

• Korean Journal of Veterinary Research
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• v.61 no.4
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• pp.39.1-39.6
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• 2021

The classification of the bone pieces excavated from Gasan-ri archaeological site 1 in Jinju, presumed to be relics was investigated macroscopically. The remains of the animal bone were 3 classes (Mammalia, Aves, Amphibia), 5 orders (Carnivora, Artiodactyla, Galliformer, Rodentia, Salientia), and 6 species (Sus scrofa, Cervidae sp., Nyctereutes procyonides, Phasianidae, Rattus norvegicus caraco, Rana nigromaculata). The total weight of the animal bone remains was 1,002.80 g, with the identified bones comprising 975.30 g and an identification rate of 97.26%. A total of 447 animal bone fragments were identified, including 204 bone pieces of S. scrofa (468.00 g, 47.99%), 102 bone pieces of Cervidae sp. (453.79 g, 46.53%), 68 bone pieces of R. nigromaculata (4.69 g, 0.48%), 59 bone pieces of N. procyonides (47.14 g, 4.83%), 9 bone pieces of Phasianidae (0.98 g, 0.10%), and 5 bone pieces of Rattus norvegicus caraco (0.70 g, 0.07%). The bone pieces of the animal relics consisted of 81 skull (18.12%), 161 axial skeleton (36.02%), 64 forelimb (14.32%), and 141 hindlimb (31.54%) fragments. The archaeological significance of the animal bones excavated in this investigation was that wild boars and deer were presumed to have been mainly used animals in the Gasan-ri area of Jinju during the Three Kingdoms period.

• Journal of Animal Science and Technology
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• v.54 no.6
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• pp.463-469
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• 2012

This study utilized a standardization and cluster analysis technique for the selection and classification of beneficial bacteria. A set of synthetic data consisting of 100 individual variables with three characteristics was created for analysis. The three characteristics assigned to each independent variable were designated to have different numeric scales, averages, and standard deviations. The variables were bacterial isolates at random, and the three characteristics were fermentation products, including cell yield, antioxidant activity of culture, and enzyme production. A standardization method utilizing a standard normal distribution equation to record fermentation yields of each isolate was employed to weight their different numeric scales and deviations. Following transformation, the data set was analyzed by cluster analysis. The Manhattan method for dissimilarity matrix construction along with complete linkage technique, an agglomerative method for hierarchical cluster analysis, was employed using statistical computing program R. A total of 100 isolates were classified into groups A, B, and C. In a comparison of the characteristics of each group, all characteristics in groups A and C were higher than those of group B. Isolates displaying higher cell yield were classified as group A, whereas those isolates showing high antioxidant activity and enzyme production were assigned to group C. The results of the cluster analysis can be useful for the classification of numerous isolates and the preparation of an isolation pool using numerical or statistical tools. The present study suggests that a simple technique can be applied to screen and select beneficial microbes using the freely downloadable statistical computing program R.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.525-538
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• 2020

A practical animal face classification system that classifies animals in image and video data is considered as a pivotal topic in machine learning. In this research, we are proposing a novel method of fully connected dual Deep Convolutional Neural Network (DCNN), which extracts and analyzes image features on a large scale. With the inclusion of the state of the art Batch Normalization layer and Exponential Linear Unit (ELU) layer, our proposed DCNN has gained the capability of analyzing a large amount of dataset as well as extracting more features than before. For this research, we have built our dataset containing ten thousand animal faces of ten animal classes and a dual DCNN. The significance of our network is that it has four sets of convolutional functions that work laterally with each other. We used a relatively small amount of batch size and a large number of iteration to mitigate overfitting during the training session. We have also used image augmentation to vary the shapes of the training images for the better learning process. The results demonstrate that, with an accuracy rate of 92.0%, the proposed DCNN outruns its counterparts while causing less computing costs.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.123-124
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• 2019

This paper proposes an improved deep learning method based on small data sets for animal image classification. Firstly, we use a CNN to build a training model for small data sets, and use data augmentation to expand the data samples of the training set. Secondly, using the pre-trained network on large-scale datasets, such as VGG16, the bottleneck features in the small dataset are extracted and to be stored in two NumPy files as new training datasets and test datasets. Finally, training a fully connected network with the new datasets. In this paper, we use Kaggle famous Dogs vs Cats dataset as the experimental dataset, which is a two-category classification dataset.