• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3384-3398
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• 2020

In recent years, as the environment has become an important issue in dealing with food, energy, and urban development, diverse environment-related applications such as environmental monitoring and ecosystem management have emerged. In such applications, automatic classification of animals using video or sound is very useful in terms of cost and convenience. So far, many works have been done for animal sounds classification using artificial intelligence techniques such as a convolutional neural network. However, most of them have dealt only with the sound of a specific class of animals such as bird sounds or insect sounds. Due to this, they are not suitable for classifying various types of animal sounds. In this paper, we propose a sound classification scheme based on a multi-feature network for classifying sounds of multiple species of animals. To do that, we first collected multiple animal sound datasets and grouped them into classes. Then, we extracted their audio features by generating mixed records and used those features for training. To evaluate the effectiveness of our scheme, we constructed an animal sound classification model and performed various experiments. We report some of the results.

• The Korean Journal of Pesticide Science
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• v.18 no.1
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• pp.48-51
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• 2014

Since the year of 2006 when the extended revision of the Codex Classification of Foods and Animal Feeds was undertaken, considerable progresses have been made in revising the Classification. This paper aimed to summarize the present status on revision of the Codex Classification of Foods and Animal Feeds, focusing remarkable achievements such as 1) the draft revision of the Codex Classification for the fruit commodity group and 2) the draft Principles and Guidance on the Selection of Representative Commodities for the Extrapolation of Maximum Residue Limits for Pesticides to Commodity Groups, adopted by the Codex Alimentarius Commission in 2012. Additionally, it included information on lists of crop group or subgroup which are holding at Step 7 and were adopted at Step 5, and further have not been yet discussed by the Codex Committee on Pesticide Residues. These information will be very helpful for a pesticide regulatory regime.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.558-572
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• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• Journal of Society of Preventive Korean Medicine
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• v.20 no.1
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• pp.125-144
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• 2016

Objectives : About 13% of the medicines used by traditional korean medicines(TKM), are called animal medicines and are derived from non-herbal sources such as animals and insects. However, the clinical use of these preparations from animal medicines is often based on tradition and belief, rather than on evidence of toxicity and efficacy. As a result, animal medicines containing toxin have caused serious problems from injecting patients with venom. Here, various venoms frequently used as TKM were reviewed in terms of their instinct toxity and tried to estimate their safety classification. Methods : The estimation of safety classification was based on human equivalent dose(HED)-based MOS (margin of safety) and clinical dose applied for patients. Results and Conclusions : Except that of snake venom due to no clinical dose, they were evaluated as class 3 for bee venom, class 4 for cantharidin, toxin from blister beetle, and class 1 for venom from scolopendrid. In conclusion, animal medicines showed a wide range of safety classification from class 1 to class 4. This wide range is estimated to result from extremely limited applications of each venom for patients because of their strong toxicity. However, it should be cautious for application in clinics since animal medicines can produce anaphylactic reactions particularly after veinous administration even with a tiny amount of venom.

• Journal of Science Education
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• v.36 no.1
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• pp.153-165
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• 2012

This study investigated the frequent mistakes and the causes of the alternative conceptions in the animal classification by using the questionnaire and interview with the middle school students (N=300). As results, some students have difficulties classifying suggested animals into vertebrates or invertebrates : snakes (31.7%), shrimps (28.3%), turtles (25.6%), frogs (24.7%), and starfish (10.7%) in order of precedence. These errors seemed to be caused by intuitive thinking over characteristics of physical motions and appearance of suggested animals, wrong inference from comparing to features of familiar animals and the lack of observation experience of the vertebrate backbone. Furthermore, the results showed that relatively many students made a mistake classifying subgroup members of vertebrates such as classifying salamanders into the class Reptilia (45.3%) and turtles into Amphibia (40.3%). It is likely that those errors are affected by ambiguousness of classification terminology (e.g. the term of Amphibia) and weak ability in relating the physiological and ecological feature to standard of classification feature. In addition, sociocultural factors could influence animal classification as 'bat in birds', 'whale in fish, and 'penguin in mammals'. The present study implied that teaching and learning animal classification may require an appropriate guide focused on activities to explore major characteristics used for the animal classification standard through providing more chances of animal observation rather than the cramming method of learning induced by technical memorizing.

• The Journal of Korean Medicine
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• v.16 no.2 s.30
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• pp.149-176
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• 1995

The results were as follow: 1. In classification of the virulence of medicines, it is the virulent animal that have a deadly poison and the rest is the animal of weak nor non-toxic. 2. In classification of the channel distribution, the most is the medicine that belongs to liver channel, the next are the stomach, lung, kidney and spleen channel. 3. In classification of four characters, the most parts are cool, common and warm medicine and there is a few that is hot and cooling. 4. In classification of five tastes, the most numerous tastes are sweet and salty and the next are acrid, bitter and sour tastes. 5. In classification of the medical action, there are few medicine of invigorating vital energy, tonic therapy and astringent and a great part of the medicine are regulating vital energy and blood, removing blood stasis and mass, clearing away heat-evil and eliminating sputum, calming the river to inhibit the wind-evil and pain control. 6. In classification of the application of cancer, the most numerous disease is the liver cancer and the next are stomach cancer, esophageal cancer, lung cancer, leukemia, uterine cancer,mastitis, brain tumor.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1521-1527
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• 2014

This paper presents to design User eXperience(UX) of anthropomorphic animal characters search system (hereinafter, AACSS) for efficient user search. To this end, meta data were utilized herein to elevate the search efficiency of multimedia information and text information. Anthropomorphic animal characters require the human elements and the animal elements, thus this paper extracted the key elements of meta data as below; phenotypic element in animal system classification (Morphologic property elements, Ecological property elements, Behavioral property elements), emotion classification, which is the trait of personification and the Step of Anthropomorphic Animal Characters.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.15-25
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• 2024

For effective analysis of animal ecosystems, technology that can automatically identify the current status of animal habitats is crucial. Specifically, animal sound classification, which identifies species based on their sounds, is gaining great attention where video-based discrimination is impractical. Traditional studies have relied on a single deep learning model to classify animal sounds. However, sounds collected in outdoor settings often include substantial background noise, complicating the task for a single model. In addition, data imbalance among species may lead to biased model training. To address these challenges, in this paper, we propose an animal sound classification scheme that combines predictions from multiple models using Focal Loss, which adjusts penalties based on class data volume. Experiments on public datasets have demonstrated that our scheme can improve recall by up to 22.6% compared to an average of single models.

• Fashion & Textile Research Journal
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• v.17 no.2
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• pp.178-190
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• 2015

The purpose of this study is to analyze the formative characteristics of animal stripes based on the colors, shapes and texture through classification standards under functional meanings, and it also aims to produce various stripe patterns based on its outcome to develop modern fashion design. For the study method, specialty publications, preceding researches and internet websites were utilized for the purpose of theoretical discussion on animal stripes. And it is also to examine functional meanings formed by animal stripes in order to analyze the characteristics of stripes; subsequently, based on the classification standard of this function, it is to identify the distinction of its colors through quantitative analysis on formative characteristics and colors. By using the following analyzed color palette of stripes, various stripe patterns were developed and designed with the sense of modern fashion while repeatedly using the shape with systematic colors and rhythms displayed on the animals. As a result of this study, it was confirmed that animal stripes possess a regularity that is systematic and can be digitized. After identifying, analyzing and systemizing formative characteristics of animal stripes, a concept of division and competition was applied; subsequently, whole new stripe patterns were developed and designed in order to further promote fashion design.