• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.322-330
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• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• Journal of Veterinary Science
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• v.21 no.2
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• pp.24.1-24.11
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• 2020

The pandemic of avian influenza viruses (AIVs) in Asia has caused enormous economic loss in poultry industry and human health threat, especially clade 2.3.4.4 H5 and H7 subtypes in recent years. The endemic chicken H6 virus in Taiwan has also brought about human and dog infections. Since wild waterfowls is the major AIV reservoir, it is important to monitor the diversified subtypes in wildfowl flocks in early stage to prevent viral reassortment and transmission. To develop a more efficient and sensitive approach is a key issue in epidemic control. In this study, we integrate multiplex reverse transcription recombinase polymerase amplification (RT-RPA) and capillary electrophoresis (CE) for high-throughput detection and differentiation of AIVs in wild waterfowls in Taiwan. Four viral genes were detected simultaneously, including nucleoprotein (NP) gene of all AIVs, hemagglutinin (HA) gene of clade 2.3.4.4 H5, H6 and H7 subtypes. The detection limit of the developed detection system could achieve as low as one copy number for each of the four viral gene targets. Sixty wild waterfowl field samples were tested and all of the four gene signals were unambiguously identified within 6 h, including the initial sample processing and the final CE data analysis. The results indicated that multiplex RT-RPA combined with CE was an excellent alternative for instant simultaneous AIV detection and subtype differentiation. The high efficiency and sensitivity of the proposed method could greatly assist in wild bird monitoring and epidemic control of poultry.

• Korean Journal of Materials Research
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• v.32 no.11
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• pp.508-514
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• 2022

Piezoelectric composite films which are enabled by inorganic piezoelectric nanomaterials-embedded polymer, have attracted enormous attention as a sustainable power source for low powered electronics, because of their ease of fabrication and flexible nature. However, the absorption of applied stress by the soft polymeric matrices is a major issue that must be solved to expand the fields of piezoelectric composite applications. Herein, a flexible and porous piezoelectric composite (piezoelectric sponge) comprised of BaTiO₃ nanoparticles and polydimethylsiloxane was developed using template method to enhance the energy conversion efficiency by minimizing the stress that vanishes into the polymer matrix. In the porous structure, effective stress transfer can occur between the piezoelectric active materials in compression mode due to direct contact between the ceramic particles embedded in the pore-polymer interface. The piezoelectric sponge with 30 wt% of BaTiO₃ particles generated an open-circuit voltage of ~12 V and a short-circuit current of ~150 nA. A finite element method-based simulation was conducted to theoretically back up that the piezoelectric output performance was effectively improved by introducing the sponge structure. Furthermore, to demonstrate the feasibility of pressure detecting applications using the BaTiO₃ particles-embedded piezoelectric sponge, the composite was arranged in a 3 × 3 array and integrated into a single pressure sensor. The fabricated sensor array successfully detected the shape of the applied pressure. This work can provide a cost-effective, biocompatible, and structural strategy for realizing piezoelectric composite-based energy harvesters and self-powered sensors with improved energy conversion efficiency.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Korean Journal of Veterinary Research
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• v.63 no.3
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• pp.21.1-21.6
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• 2023

The increasing prevalence of toxic substance-exposure in pets in South Korea endangers the health and safety of numerous companion animals, and has become a cause for concern. Notably, the annual incidence of forensic analysis in pets has increased by more than 150% in South Korea, mainly in populous regions such as Seoul, Incheon, and Gyeonggi. In response to this growing issue, veterinary forensic examinations were conducted on 549 dogs and cats from 2019 to 2022. This study revealed the presence of various toxic substances, including pesticides, insecticides, and drugs such as analgesics, anesthetics, antidepressants, and muscle relaxants, in pets. Among the 38 different toxins identified in pets, coumatetralyl, methomyl, terbufos, and buprofezin were the most frequently detected. In this study, toxic substances for pets were identified based on the "toxic agent list for humans," developed by the National Forensic Services, because no list of toxic agents for animals currently exists and data regarding potentially toxic substances for dogs and cats is limited. This is one of the limitations of this study, and necessitates the establishment of a toxic agent list for animals. Continued monitoring and research is also recommended to reveal the incidence, causes, and solutions of toxicity in animals.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.33-39
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• 2024

Inspecting cracks to determine a structure's condition is crucial for accurate safety diagnosis. However, visual crack inspection methods can be subjective and are dependent on field conditions, thereby resulting in low reliability. To address this issue, this study automates the detection of concrete cracks in image data using ResNet, FPN, and the Mask R-CNN components as the backbone, neck, and head of a convolutional neural network. The performance of the proposed model is analyzed using the intersection over the union (IoU). The experimental dataset contained 1,203 images divided into training (70%), validation (20%), and testing (10%) sets. The model achieved an IoU value of 95.83% for testing, and there were no cases where the crack was not detected. These findings demonstrate that the proposed model realized highly accurate detection of concrete cracks in image data.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.12
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• pp.1895-1904
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• 2015

Foodborne illness associated with food service establishments is an important food safety issue in Korea. In this study, foodborne pathogens (Bacillus cereus, Clostridium perfringens, Escherichia coli, pathogenic Escherichia coli, Listeria monocytogenes, Salmonella spp., Staphylococcus aureus, and Vibrio parahaemolyticus) and hygiene indicator organisms [total viable cell counts (TVC), coliforms] were analyzed for food and environmental samples from foodservice establishments at schools in Gyeonggi province. Virulence factors and antimicrobial resistance of detected foodborne pathogens were also characterized. A total of 179 samples, including food (n=66), utensil (n=68), and environmental samples (n=45), were collected from eight food service establishments at schools in Gyeonggi province. Average contamination levels of TVC for foods (including raw materials) and environmental samples were 4.7 and 4.0 log CFU/g, respectively. Average contamination levels of coliforms were 2.7 and 4.0 log CFU/g for foods and environmental swab samples, respectively. B. cereus contamination was detected in food samples with an average of 2.1 log CFU/g. E. coli was detected only in raw materials, and S. aureus was positive in raw materials as well as environmental swab samples. Other foodborne pathogens were not detected in all samples. The entire B. cereus isolates possessed at least one of the diarrheal toxin genes (hblACD, nheABC, entFM, and cytK enterotoxin gene). However, ces gene encoding emetic toxin was not detected in B. cereus isolates. S. aureus isolates (n=16) contained at least one or more of the tested enterotoxin genes, except for tst gene. For E. coli and S. aureus, 92.7% and 37.5% of the isolates were susceptible against 16 and 19 antimicrobials, respectively. The analyzed microbial hazards could provide useful information for quantitative microbial risk assessment and food safety management system to control foodborne illness outbreaks in food service establishments.

• Analytical Science and Technology
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• v.35 no.3
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• pp.136-142
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• 2022

According to media reports, the carcasses of euthanized abandoned dogs were processed at high temperature and pressure to make powder, and then used as feed materials (meat and bone meal), raising the possibility of residuals in the feed of the anesthetic ketamine and dexmedetomidine used for euthanasia. Therefore, a simultaneous analysis method using QuEChERS combined with high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry was developed for rapid residue analysis. The method developed in this study exhibited linearity of 0.999 and higher. Selectivity was evaluated by analyzing blank and spiked samples at the limit of quantification. The MRM chromatograms of blank samples were compared with those of spiked samples with the analyte, and there were no interferences at the respective retention times of ketamine and dexmedetomidine. The detection and quantitation limits of the instrument were 0.6 ㎍/L and 2 ㎍/L, respectively. The limit of quantitation for the method was 10 ㎍/kg. The results of the recovery test on meat and bone meal, meat meal, and pet food showed ketamine in the range of 80.48-98.63 % with less than 5.00 % RSD, and dexmedetomidine in the range of 72.75-93.00 % with less than 4.83 % RSD. As a result of collecting and analyzing six feeds, such as meat and bone meal, prepared at the time the raw material was distributed, 10.8 ㎍/kg of ketamine was detected in one sample of meat and bone meal, while dexmedetomidine was found to have a concentration below the limit of quantitation. It was confirmed that the detected sample was distributed before the safety issue was known, and thereafter, all the meat and bone meal made with the carcasses of euthanized abandoned dogs was recalled and completely discarded. To ensure the safety of the meat and bone meal, 32 samples of the meat and bone meal as well as compound feed were collected, and additional residue investigations were conducted for ketamine and dexmedetomidine. As a result of the analysis, no component was detected. However, through this investigation, it was confirmed that some animal drugs, such as anesthetics, can remain without decomposition even at high temperature and pressure; therefore, there is a need for further investigation of other potentially hazardous substances not controlled in the feed.

• Journal of Food Hygiene and Safety
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• v.37 no.3
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• pp.181-188
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• 2022

Despite the recent increase in the consumption level of the processed meat-byproducts, the health and safety issue has consistently been raised in the processes of production, distribution and consumption. The purpose of this study is to analyze and evaluate the microbiological hazard elements in the Korean sausage, "Sundae," to present not only the safety standard of meat by-product vendors based on HACCP (Hazard Analysis Critical Control Point), but also the quality control criteria and sanitary arrangements of small manufacturers. For the study, the microbiological hazards in 24 raw materials, 7 manufacturing processes, 40 facilities and tools, 17 workplace environment, and 12 workers were analyzed. The analysis revealed the hazardous elements in the initial stages with 6.28 and 4.07 log CFU/g of total aerobic count and coliforms, respectively, detected from the porcine blood and 3.23 log CFU/g of coliforms from the porcine small intestines. The result also showed that the total aerobic counts and coliforms in the process of mixing and filling process exceeds the standards in the hygiene guidelines by Natick with the total aerobic counts of 5.23, 5.45 log CFU/g, and the coliforms of 3.25, and 3.31 log CFU/g, respectively. Although the detected total aerobic count and the coliforms in the filling and washing rooms exceeded the standards, it was found that the total aerobic count was significantly reduced by 98% after cleaning and disinfecting and no coliforms was detected in any process thereafter. In order to achieve high level of safety in the manufacturing processes of Sundae, the separation of washing and disinfection room from the other sections and the sanitation control of the workers must be preceded, along with strict monitoring in the storage and distribution processes. The study raises necessity for additional studies for the safety evaluation of the processed meat-byproducts and further researches on the validity of the critical limits.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.