• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.327-330
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• 2018

국내 축산 농가들은 대부분 돼지우리의 구역을 나눈 후 해당 구역별로 30여 마리의 돼지들을 합사하여 사육하고 있다. 따라서 전염성이 강한 호흡기 질병이 발병하게 되면 돼지우리 전체로 확산되어 심각한 피해가 발생하게 된다. 본 논문에서는 돼지우리에서 발생하는 다양한 소음에도 강인한 소리 기반의 호흡기 질병 탐지 시스템을 제안한다. 제안된 시스템은 먼저, 소리 신호에서 스펙트로그램 정보를 추출하고, 이를 CNN을 기반으로 돼지 호흡기 질병에 효과적인 특징 벡터를 생성한다. 마지막으로, 추출된 특징 벡터를 MLP에 적용하여 해당 호흡기 질병을 탐지 및 식별과정을 수행한다. 본 연구의 실험 결과, 다양한 잡음 환경에서도 돼지 호흡기 질병 탐지 및 식별이 가능함을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.271-280
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• 2014

Foot-and-mouth Disease (FMD) is a highly infectious and fatal viral livestock disease that affects cloven-hoofed animals domestic and wild and the FMD outbreak in Korea in 2010/2011 was a disastrous incident for the country and the economy. Thus, efforts at the national level are put to prevent foot-and-mouth disease and to reduce the damage in the case of outbreak. As one of these efforts, it is useful to study the spread of the disease by using probabilistic model. In fact, after the FMD epidemic in the UK occurred in 2001, many studies have been carried on the spread of the disease using a variety of stochastic models as an effort to prepare future outbreak of FMD. However, for the FMD outbreak in Korea occurred in 2010/2011, there are few study by utilizing probabilistic model. This paper assumes a stochastic spatial-temporal susceptible-infectious-removed (SIR) epidemic model for the 2010/2011 FMD outbreak to understand spread of the disease. Since data on infections of FMD disease during 2010/2011 outbreak of Aniaml and Plant Quarantine Agency and on the livestock farms from the nationwide census in 2011 of Statistics Korea do not have detail informations on address or missing values, we generate detail information on address by randomly allocating farms within corresponding Si/Gun area. The kernel function is estimated using the infection data and by using simulations, the susceptibility and transmission of the spatial-temporal stochastic SIR models are determined.

• Journal of the Korean Geographical Society
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• v.47 no.5
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• pp.677-699
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• 2012

Climate changes may cause substantial changes in spatial patterns and distribution of vector-borne diseases (VBD's), which will result in a significant threat to humans and emerge as an important public health problem that the international society needs to solve. As global warming becomes widespread and the Korean peninsula characterizes subtropical climate, the potentials of climate-driven disease outbreaks and spread rapidly increase with changes in land use, population distributions, and ecological environments. Vector-borne diseases are typically infected by insects such as mosquitoes and ticks, and infected hosts and vectors increased dramatically as the habitat ranges of the VBD agents have been expanded for the past 20 years. Medical geography integrates and processes a wide range of public health data and indicators at both local and regional levels, and ultimately helps researchers identify spatiotemporal mechanism of the diseases determining interactions and relationships between spatial and non-spatial data. Spatial epidemiology is a new and emerging area of medical geography integrating geospatial sciences, environmental sciences, and epidemiology to further uncover human health-environment relationships. An introduction of GIS-based disease monitoring system to the public health surveillance system is among the important future research agenda that medical geography can significantly contribute to. Particularly, real-time monitoring methods, early-warning systems, and spatial forecasting of VBD factors will be key research fields to understand the dynamics of VBD's.

• Journal of Life Science
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• v.28 no.5
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• pp.627-637
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• 2018

Motility and chemotaxis are crucial for disease development in many motile pathogens, including spirochetes. In many bacteria, motility is provided by flagella rotation, which is controlled by a chemotaxis-signal-transduction system. Thus, motility and chemotaxis are inextricably linked. Spirochetes are a unique group of bacteria with distinctive flat-wave morphology and corkscrew-like locomotion. This unusual motility pattern is believed to be important for efficient motility within the dense tissues through which these spirochetes preferentially disseminate in a host. Unlike other externally flagellated bacteria-where flagella are in the ambient environment-the flagella of spirochetes are enclosed by the outer membrane and thus are called periplasmic flagella or endoflagella. Although motilityand chemotaxis-associated genes are well studied in some bacteria, the knowledge of how the spirochete achieves complex swimming and the roles of most of the putative spirochetal chemotaxis proteins are still elusive. Recently, cutting-edge imaging methods and unique genetic manipulations in spirochetes have helped to unravel the mystery of motility and chemotaxis in spirochetes. These contemporary advances in understanding the motility and chemotaxis of spirochetes in a host's persistence and disease process are highlighted in this review.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.