• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.722-727
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• 2013

Medical treatment for animals is very difficult as the opinions of pets' masters take priority over treatment regardless of the seriousness of pets' disease or the needs of medical treatment. In case that a pet has heart disease, especially, it is difficult to get the direct answer from the pet's master on the decision or confirmation of treatment. For those reasons, it is almost impossible to predict and treat the pet before an emergency like the heart failure or an unexpected death happens. Using stethoscope can be the first diagnosis method to check the heart or any kinds of disease inside the body. High-tech equipments like CT, X-ray or Ultrasound can be used, but they can only be used as a second choice of diagnosis method since it requires professional skills and its high price. That's why stethoscope is still the best diagnostic tool when one makes the first diagnosis. In this study, we give a detailed account of digital diagnosis system in which veterinarians can analyze the sound from stethoscope without bringing it to their ears and make a diagnosis wherever they are. And we suggest a new concept of diagnosis system surrounding, which shows the relativeness of disease through Level Crossing Rate(LCR) and energy level from the stethoscope sound made in this system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• Journal of Food Hygiene and Safety
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• v.33 no.2
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• pp.89-93
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• 2018

High sodium dietary habits may cause various diseases, thereby threatening the public health. Various efforts have been made to control high sodium use in diets, but few studies have been conducted on health communication efforts to modify such habits. This study looks for suggestions for diet improvement education by examining whether or not high sodium dietary habits can be predicted by a consumer's perception on the threat and controllability of high sodium diets. In this study, a questionnaire was developed to measure the severity, vulnerability, efficiency, efficacy, and behavioral tendencies of the consumer, which were subscales of the protective motivation theory. The questionnaire was given to university students and their families in Chungnam Province. The results of a statistical analysis were as follows: First, more young people preferred high-sodium diets than older people. Second, the correlation analysis showed that older people knew that they were vulnerable to the negative effects of high sodium diets, but they would not change their dietary habits until they were confident that they could control the high-sodium diet. Third, the structural model analysis showed that the higher the coping perception was, the lower was the tendency to consume a high-sodium diet. These results suggest that in the effort to reduce high-sodium diets, it is more effective to provide viable information and improve efficacy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.292-301
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• 2016

Because damages arising from the occurrence of foot-and-mouth disease (FMD) are very great, it is essential to make a preemptive diagnosis to cope with it in order to minimize those damages. The main symptoms of foot-and-mouth disease are body temperature increase, loss of appetite, formation of blisters in the mouth, on hooves and breasts, etc. in a cow or a bull, among which the body temperature check is the easiest and quickest way to detect the disease. In this paper, an algorithm to detect FMD from the hooves of cattle was developed and implemented for preemptive coping with foot-and-mouth disease, and a hoof check test is conducted after the installation of a high-resolution camera module, a thermo-graphic camera, and a temperature/humidity module in the cattle shed. Through the algorithm and system developed in this study, it is possible to cope with an early-stage situation in which cattle are suspected as suffering from foot-and-mouth disease, creating an optimized growth environment for cattle. In particular, in this study, the system to cope with FMD does not use a portable thermo-graphic camera, but a fixed camera attached to the cattle shed. It does not need additional personnel, has a function to measure the temperature of cattle hooves automatically through an image algorithm, and includes an automated alarm for a smart phone. This system enables the prediction of a possible occurrence of foot-and-mouth disease on a real-time basis, and also enables initial-stage disinfection to be performed to cope with the disease without needing extra personnel.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.1-6
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• 2019

In modern society, lifestyle and individuality are important, and personalized lifestyle and patterns are emerging. The number of people with articulation diseases is increasing due to wrong living habits. In addition, as the number of households increases, there is a case where emergency care is not received at the appropriate time. We need information that can be managed by ourselves through accurate analysis according to the individual's condition for health and disease management, and care appropriate to the emergency situation. It is effectively used for classification and prediction of data using CNN in deep learning. CNN differs in accuracy and processing time according to the data features. Therefore, it is necessary to improve processing speed and accuracy for real-time healthcare. In this paper, we propose motion monitoring using Mask R-CNN for articulation disease management. The proposed method uses Mask R-CNN which is superior in accuracy and processing time than CNN. After the user's motion is learned in the neural network, if the user's motion is different from the learned data, the control method can be fed back to the user, the emergency situation can be informed to the guardian, and appropriate methods can be taken according to the situation.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.3
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• pp.257-265
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• 2021

The COVID-19 virus is a β-genus virus that causes infection by mediating the angiotensin convertible enzyme 2 (ACE2) receptor, which is distributed in large numbers in the human respiratory tract. The disease requires effective post-management of antibody production by complete healers and vaccinators because there is no perfect remedy for the virus infection. This study aimed to develop recombinant proteins specifically responsive to neutralizing antibodies in clinical specimens and use them to develop a rapid diagnostic kit to diagnose neutralizing antibodies quickly and conveniently against the COVID-19 virus and confirm the possibility of commercialization through a performance evaluation. Rapid diagnostic kits using COVID-19 S1 RBD recombinant proteins can be applied to rapid diagnostic kits, with positive percentage agreement (PPA) and negative percentage agreement (NPA) of 100% and 98.3%, respectively, compared to the U.S. FDA-approved ELISA kits. If the performance of the rapid diagnostic kit is improved and neutralizing antibodies can be analyzed quantitatively using quantitative analysis equipment, it can be used as important data to predict immunity to the COVID-19 virus and determine additional vaccinations.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.241-262
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• 2021

This paper reviewed the necessity of a information and communication technology (ICT)-based smart livestock system as a development strategy for the animal life industry in the future. It also predicted the trends of livestock and animal food until 2050, 30 years later. Worldwide, livestock raising and consumption of animal food are rapidly changing in response to population growth, aging, reduction of agriculture population, urbanization, and income growth. Climate change can change the environment and livestock's productivity and reproductive efficiencies. Livestock production can lead to increased greenhouse gas emissions, land degradation, water pollution, animal welfare, and human health problems. To solve these issues, there is a need for a preemptive future response strategy to respond to climate change, improve productivity, animal welfare, and nutritional quality of animal foods, and prevent animal diseases using ICT-based smart livestock system fused with the 4th industrial revolution in various aspects of the animal life industry. The animal life industry of the future needs to integrate automation to improve sustainability and production efficiency. In the digital age, intelligent precision animal feeding with IoT (internet of things) and big data, ICT-based smart livestock system can collect, process, and analyze data from various sources in the animal life industry. It is composed of a digital system that can precisely remote control environmental parameters inside and outside the animal husbandry. The ICT-based smart livestock system can also be used for monitoring animal behavior and welfare, and feeding management of livestock using sensing technology for remote control through the Internet and mobile phones. It can be helpful in the collection, storage, retrieval, and dissemination of a wide range of information that farmers need. It can provide new information services to farmers.

• Food Science and Industry
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• v.55 no.2
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• pp.154-165
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• 2022

Humans have been breeding the mulberry silkworm for the long period of time to obtain silk fabric and nutrient-rich pupae. Currently, silkworm larvae, pupae, and silk-Fibroin hydrolysates are registered as food raw materials, while silkworm feces and Bombyx batryticatus are registered as Korean traditional medicines. Among sericulture products, individually recognized health functional food ingredients include silk-protein acid-hydrolysates for immunity enhancement, Fibroin-hydrolysates for memory improvement, and freeze-dried 5^th instar and 3^rd-day-silkworm powder for lowering-blood sugar. Recently, HongJam produced by steaming and freeze-drying mature silkworms were reported to have various health-promoting effects such as preventing the onset of Alzheimer's disease and Parkinson's disease, enhancing gastro-intestinal functions, improving skin-whitening and hair growth, and extending healthspan. By consuming silkworm products with various health-promoting effects, it is possible to increase the healthspan of human beings, thereby reducing personal and national medical expenses, resulting in increasing the individual's happiness.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.401-410
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• 2019

This study was conducted to identify factors affecting nursing performance of delirium among nursing students. A total of 252 fourth year students were recruited from nursing department in Gwangju. Structured questionnaire was self-administrated from April to September, 2017. The used statistical analysis were t-test, ANOVA, Pearson's coefficient and multiple regression analysis. Knowledge of delirium 29.0±7.24, self-confidence in the care for delirium 71.65±28.55 and nursing performance level for patients with delirium was 41.16±8.97. Nursing performance of delirium had significant positive correlations with delirium knowledge, self-confidence of delirium care. In multiple regression analysis, nursing experience for delirium patients, self-confidence of delirium care, practice experience in intensive care unit, use of nursing diagnosis related to delirium, and satisfaction of clinical practice were significant factors of nursing performance of delirium explaining 29.8% of the variables. In conclusion, to enhance nursing performance of delirium, it is necessary to develop educational program for increasing nursing experience for delirium patients during clinical practice and self-confidence of delirium care.