• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.923-935
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• 2022

This paper deals with detection and classification of leaf diseases for phenomics systems. As the smart farm systems of plants are increased, It is important to determine quickly the abnormal growth of plants without supervisors. This paper considers the color distribution and shape information of leaf diseases, and designs two deep leaning networks in training the leaf diseases. In the first step, color distribution of input image is analyzed for possible diseases. In the second step, the image is first partitioned into small segments using mean shift clustering, and the color information of each segment is inspected by the proposed Color Network. When a segment is determined as disease, the shape parameters of the segment are extracted and inspected by proposed Shape Network to classify the leaf disease types in the third step. According to the experiments with two types of diseases (frogeye/rust and tipburn) for apple leaves and iceberg, the leaf diseases are detected with 92.3% recall for a segment and with 99.3% recall for an input image where there are usually more than two disease segments. The proposed method is useful for detecting leaf diseases quickly in the smart farm environment, and is extendible to various types of new plants and leaf diseases without additional learning.

• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.39-66
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• 2023

New media moved the place of social exchange to the Internet, allowing large groups to communicate in one place beyond the limits of time and space. Recent studies have also reported cases in which human social abilities do not keep up with the expansion of group scale through social media. In this context, current study investigated how human perception of social information is affected by the expansion of the group scale in the context of majority bias. Using Internet-based task, the psychological processes that group ratio and group size are perceived and affect majority-biased social information use were investigated, and whether group scale moderates those processes was examined. The group ratio has a positive effect on the majority bias, and the relationship was partially mediated by ratio perception. Group scale did not moderate the relationship between group ratio and ratio perception. On the other hand, the correlation between group size and majority-biased social information use was not significant. Group scale moderates group size perception. The group size and size perception showed positive correlation under the smaller group scale condition. However under the extended group scale condition, the perceived group size became significantly lower and lost its correlation with group size. These results provide evidence that the psychological mechanism related to group size perception was not properly responding to the expansion of the group scale. Furthermore, the possibility of a specific psychological mechanism for processing group size information and the form of information input specifically accepted by majority bias were discussed from perspective of evolutionary psychology.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.85-92
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• 2023

Recently, interest in artificial intelligence has increased due to the development of artificial intelligence fields such as ChatGPT and self-driving cars. However, there are still many unknown elements in training process of artificial intelligence, so that optimizing the model requires more time and effort than it needs. Therefore, there is a need for a tool or methodology that can analyze the weight changes during the training process of artificial intelligence and help out understatnding those changes. In this research, I propose a visualization system which helps people to understand the accumulated weight changes. The system calculates the weights for each training period to accumulates weight changes and stores accumulated weight changes to plot them in 3D space. This research will allow us to explore different aspect of artificial intelligence learning process, such as understanding how the model get trained and providing us an indicator on which hyperparameters should be changed for better performance. These attempts are expected to explore better in artificial intelligence learning process that is still considered as unknown and contribute to the development and application of artificial intelligence models.

• Smart Media Journal
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• v.12 no.10
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• pp.93-101
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• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• Journal of the Korean Dietetic Association
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• v.14 no.2
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• pp.139-151
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• 2008

Presently, media for sanitation education consisting of a sanitation manual and a CD-ROM intended for restaurant employers and employees was developed and evaluated. The sanitation manual consisted of five principles: prevention of foodborne illness, personal hygiene, control of food production, instrument and equipment cleaning and sanitation, and management of environmental sanitation. The CD-ROM was composed of animations detailed real-life examples of Salmonella, Staphylococcus aureus, and Norovirus foodborned illness outbreaks; slides summarizing the five principles of the manual; and a poster entitled You can prevent foodborne illness listing and describing the principles. A 15 question evaluation survey was developed to gauge the efficacy of the animations. The survey was divided into five sections on comprehension of the instructions, content organization concerning understanding, content organization concerning the information presented, content organization concerning retention of interest (concentration), and recommendations concerning concentration. Ranked on a 5-point scale the survey produced a mean value of 3.80$\pm$0.39 and individual scores of 3.92$\pm$0.45 (learning instruction), 3.86$\pm$0.48 (understanding), 3.82$\pm$0.52 (information), 3.75$\pm$0.49 (concentration), and 3.67$\pm$0.58 (concentration-recommendation). Overall, evaluation results of the animation were good and easy to understand, with only a few respondents electing to watch the animations more than once. In terms of continuous and recurring education, sanitation training programs should be easy to learn and contain sufficient and specific examples of the importance of sanitation in achieving food safety.

• The Korean Journal of Food And Nutrition
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• v.25 no.4
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• pp.957-967
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• 2012

The purpose of this study is to offer youth with the basic materials for developing nutrition education programs and to help domestic science teachers in schools implement effective dietary education by examining their awareness of food additives. In the source of acquiring knowledges on food additives, the results were through mass media, technology and home economics teachers, and family members in order. The majority of students thought that they don't need the education about food additives. As effective teaching methods, they first selected the use of visual media, followed by experimental cooking classes, field trip, and inquiry lessons using processed foods. As a result of analyzing the education on food additives in accordance with demographic factors, more number of female students, compared to male students depicted the need for education on food additives. Further, the lower the cost students spend on purchasing processed foods per day, the higher the necessity of the education they indicate. The necessity of education content on food additives showed significant difference depending on the cost of buying processed foods, and the degree of interest and help real-life according to gender indicated significant differences. The satisfaction with education on food additives, which was implemented in home economics education revealed significant differences according to gender. This study aims to provide the basic data for the development and research of educational programs regarding good eating habits among the general youth. However, there are limitations to the presentation of the practical training program. For this reason, based on the results of this study, further studies should follow this study in order to develop and study educational programs related to food additives for teaching and learning purposes.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.10-18
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• 2014

As the standard of living and leisure time of contemporary people is increasing, people who have interest not only in physical health activities but also in mental health activities are increasing. Also, people who have interest in sports activities, which include entertaining factors are gradually increasing. SporTainment is a compound of the words 'Sports' and 'Entertainment' and it possesses the meaning of doing sports and entertainment simultaneously. In addition, in the immersive media technology field, researches, which increase the feeling of existence and immersion through the stimulation of the consumer's emotional feedback technology, have been actively conducted. Such, emotional feedback technologies are based on fun and excitement and are activities, in which learning effects are included such as games, education, national defense etc. In addition, the emotional feedback technology is expanding in applied services, which maximize the feeling of existence and immersion of the consumers by adding reality effects in the virtual environment. In this research, the presence of the user was increased by developing a realistic bike simulation for the use of SporTainment, which combine emotional effects and reappearance devices in the bike simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.403-409
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• 2016

Recently, harmful content (such as images and photos of nudes) has been widely distributed. Therefore, there have been various studies to detect and filter out such harmful image content. In this paper, we propose a new method using Haar-like features and an AdaBoost algorithm for robustly extracting navel areas in a color image. The suggested algorithm first detects the human nipples through color information, and obtains candidate navel areas with positional information from the extracted nipple areas. The method then selects real navel regions based on filtering using Haar-like features and an AdaBoost algorithm. Experimental results show that the suggested algorithm detects navel areas in color images 1.6 percent more robustly than an existing method. We expect that the suggested navel detection algorithm will be usefully utilized in many application areas related to 2D or 3D harmful content detection and filtering.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.63-69
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• 2020

The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.