• Korean Journal of Heritage: History & Science
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• v.52 no.1
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• pp.170-189
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• 2019

Sochi Heo Ryun (小癡 許鍊, 1808-1893) was a literary artist of Chinese paintings of the Southern School during the late Joseon dynasty and the founder of paintings in the literary artist's style of Jindo County in South Jeolla Province. He was also a professional literary artist who acquired both learning and painting techniques under Choui (a Zen priest) and Kim Jeong-hee's teachings. Heo Ryun's landscape paintings were influenced by Kim Jung -hee. However, his ink orchid paintings, which he began producing in his later years, were not related to the 'Ink Orchid Paintings of Chusa (秋史蘭)'. His ink orchid paintings as a whole drew attention as he followed the old methods but still used rough brush strokes . Ordinary orchids were drawn based on Confucian content. However, his Jebal (題跋) and seal (印章) contain not only Confucian characters but also Taoist and Buddhist meanings. Therefore, it is possible to guess his direction of life and his private world of suffering. Ryun's ink orchid paintings reflected a variety of philosophies and aesthetic sensibilities. He went through a process of stylistic change over time and formed an 'Ink Orchid Painted Thought' in later life. The main characteristic of Sochi's ink orchid paintings is that he formed his own special methods for orchid paintings by mimicking the Manuals of Paintings. He drew orchids with his fingers in the beginning. Then, Jeongseop, Lee Ha-eung, Cho Hee-ryong, and others developed an organic relationship with the painting style of ink orchid paintings. Then in later years, orchid paintings reached the point of 'Picture Painted Thought (寫意畵)'. The above consideration shows that ink orchid paintings, which he produced until the end of his life, were the beginning of his mental vision and will to realize the image of a literal artist.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.241-253
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• 2019

The purpose of this study was to examine the phenomena that occurred when the students were practicing flip-learning lessons and to present the good points and the unsatisfactory points to improve the students' learning pleasure. Therefore, it is aimed to provide the basic data and the advantages and disadvantages which are needed to apply the flip learning method which is newly emerging recently to the university instruction. The method of this study was a questionnaire survey to understand the perception of flip learning. Based on the results of this study, the following conclusions are presented. First, the experience of flip learning instruction was first encountered by 50% of college students. Second, the students showed a very low tendency in practicing the flip learning instruction(video watching) the instructor intended. Third, college students have a habit of learning that they are not ready for pre-study of the subject. Fourth, the perception of flip learning lesson through the provision of video was highly positive. Fifthly, flip learning lessons have the advantage of being able to learn regardless of the time and place that they have, but they are not actively involved if they are not actually reflected in the test or grades. In conclusion, it was found that college students became accustomed to the incentive-style lessons due to the application of various learning techniques from elementary school age, making it difficult to participate in voluntary learning.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.78-82
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• 2010

Purpose: Recently, South Korea has seen a rapidly increased incidence of both breast and thyroid cancers. As a result, the I-131 scan and lymphoscintigraphy have been performed more frequently. Although this type of diagnostic imaging is prominent in that visualizes pathological conditions, which is similar to previous nuclear diagnostic imaging techniques, there is not much anatomical information obtained. Accordingly, it has been used in different ways to help find anatomical locations by transmission scan, however the results were unsatisfactory. Therefore, this study aims to realize an imaging technique which shows more anatomical information through the fusion of gamma and realistic imaging. Materials and Methods: We analyzed the data from patients who were examined by the lymphoscintigraphy and I-131 additional scan by Symbia Gamma camera (SIEMENS) in the nuclear medicine department of the National Cancer Center from April to July of 2009. First, we scanned the same location in patients by using a miniature camera (R-2000) in hyVISION. Afterwards, we scanned by gamma camera. The data we obtained was evaluated based on the scanning that measures an agreement of gamma and realistic imaging by the Gamma Ray Tool fusion program. Results: The amount of radiation technicians and patients were exposed was generated during the production process of flood source and applied transmission scan. During this time, the radiation exposure dose of technicians was an average of 14.1743 ${\mu}Sv$, while the radiation exposure dose of patients averaged 0.9037 ${\mu}Sv$. We also confirmed this to matching gamma and realistic markers in fusion imaging. Conclusion: Therefore, we found that we could provide imaging with more anatomical information to clinical doctors by fusion of system of gamma and realistic imaging. This has allowed us to perform an easier method in which to reduce the work process. In addition, we found that the radiation exposure can be reduced from the flood source. Eventually, we hope that this will be applicable in other nuclear medicine studies. Therefore, in order to respect the privacy of patients, this procedure will be performed only after the patient has agreed to the procedure after being given a detailed explanation about the process itself and its advantages.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.1-12
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• 2019

The health benefits associated with consumption of fresh produce have been clearly demonstrated and encouraged by international nutrition and health authorities. However, since fresh produce is usually minimally processed, increased consumption of fresh fruits and vegetables has also led to a simultaneous escalation of foodborne illness cases. According to the report by the World Health Organization (WHO), 1 in 10 people suffer from foodborne diseases and 420,000 die every year globally. In comparison to other processed foods, fresh produce can be easily contaminated by various routes at different points in the supply chain from farm to fork. This review is focused on the identification and characterization of possible sources of foodborne illnesses from chemical, biological, and physical hazards and the applicable methodologies to detect potential contaminants. Agro-chemicals (pesticides, fungicides and herbicides), natural toxins (mycotoxins and plant toxins), and heavy metals (mercury and cadmium) are the main sources of chemical hazards, which can be detected by several methods including chromatography and nano-techniques based on nanostructured materials such as noble metal nanoparticles (NMPs), quantum dots (QDs) and magnetic nanoparticles or nanotube. However, the diversity of chemical structures complicates the establishment of one standard method to differentiate the variety of chemical compounds. In addition, fresh fruits and vegetables contain high nutrient contents and moisture, which promote the growth of unwanted microorganisms including bacterial pathogens (Salmonella, E. coli O157: H7, Shigella, Listeria monocytogenes, and Bacillus cereus) and non-bacterial pathogens (norovirus and parasites). In order to detect specific pathogens in fresh produce, methods based on molecular biology such as PCR and immunology are commonly used. Finally, physical hazards including contamination by glass, metal, and gravel in food can cause serious injuries to customers. In order to decrease physical hazards, vision systems such as X-ray inspection have been adopted to detect physical contaminants in food, while exceptional handling skills by food production employees are required to prevent additional contamination.