• Korean journal of applied entomology
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• v.58 no.3
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• pp.183-187
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• 2019

To establish the cooperative monitoring network which can investigate introductions or outbreaks of high risk insect pests into Korea, seven universities, Gyeongsang National University, Kunsan National University, Seoul National University, Sunchon National University, Andong National University, Jeju National University, and Chungbuk National University, carried out seven regions' monitoring about nine high risk insect pests, Aceria diospyri, Bactrocera dorsalis, Bactrocera minax, Bactrocera tsuneonis, Cydia pomonella, Lobesia botrana, Proeulia sp., Solenopsis invicta, Stephanitis takeyai, from June to October in 2018. A total of 7,560 traps/visual scouting were investigated in 315 points of 105 local sites of seven regions, resulting the nine species, A. diospyri, B. dorsalis, B. minax, B. tsuneonis, C. pomonella, L. botrana, Proeulia sp., S. invicta, and S. takeyai, were not detected. From this study, we established the nationwide monitoring system which can early detect high risk insect pests and secured a bridgehead for monitoring invasive insect pests passing the border.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.1-17
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• 2018

This study was conducted to interpret the landscape of Songge Byeoleop(Korean villa) garden at Jogyedong, Bukhansan near Seoul which was built in the mid 17C. to restore through the literature reviews and field surveys. The results were as follows; Songge Byeoleop garden was a royal villa, constructed at King Injo24(1646) of Joseon dynasty by prince Inpyeong(麟坪大君), Lee, Yo(李?, 1622~1658), the third son of King Injo who was a brother of King Hyojong. It was a royal villa, Seokyang-lu under Mt. Taracsan of Gyendeokbang, about 7km away in the straight line from main building. It was considered that the building system was a very gorgeous with timber coloring because of owner's special situation who was called the great prince. The place of Songge Byeoleop identity and key landscape of the place were consisted with Gucheon waterfall and the sound of the water with multi-layered waterfall which might be comparable to the waterfall of Yeosan in China. After the destruction of the building, the place was used for the royal tomb quarry, but there was a mark stone for forbidden quarry. The Inner part of Songge Beoleop, centered with Jogedongcheon, Chogye-dong, composted beautifully with the natural sceneries of Gucheon waterfall, Handam and Changbeok, and artificial structures, such as Bihong-bridge, Boheogak, Yeonghyudang and Gyedang. In addition, the existing Chinese characters, 'Songge Beoleop' and 'Gucheoneunpog' carved in the rocks are literary languages and place markings symbolizing with the contrast of the different forests and territories. They gave the names of scenery to the rock and gave meaning to them. Particularly, Gucheon waterfall which served as a visual terminal point, is a cascade type with multi-staged waterfall. and the lower part shows the topographical characteristics of the Horse Bowl-shaped jointed with port-holes. On the other hand, the outer part is divided into the spaces for the main entrance gate, a hanging bridge character, a bridge connecting the inside and the outside, and Yeonghyudang part for the purpose of living. Also in the Boheogak area, dual view frame structures are made to allow the view of the four sides including the width and the perimeter of the villa. In addition, at the view point in Bihong-bridge, the Gucheon water fall divides between the sacred and profane, and crosses the Bihong-bridge and climbs to the subterranean level.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.271-286
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• 2023

The global COVID-19 pandemic has transformed face masks from situational accessories to indispensable items in daily life, prompting a shift in public perception and behavior. While the relaxation of mandatory mask-wearing regulations is underway, a significant number of individuals continue to embrace face masks, turning them into a form of personal expression and identity. This phenomenon has given rise to the Fashion Mask industry, characterized by unique designs and colors, experiencing rapid growth in the market. However, existing research on masks is predominantly focused on their efficacy in preventing infection or exploring attitudes during the pandemic, leaving a gap in understanding consumer preferences for mask design. We address this gap by investigating consumer perceptions and preferences for two prevalent mask designs-horizontal fold flat masks and vertical fold flat masks. Through a comprehensive approach involving surveys and eye-tracking experiments, we aim to unravel the subtle differences in how consumers perceive these designs. Our research questions focus on determining which design is more appealing and exploring the reasons behind any observed differences. The study's findings reveal a clear preference for vertical fold flat masks, which are not only preferred but also perceived as unique, sophisticated, three-dimensional, and lively. The eye-tracking analysis provides insights into the visual attention patterns associated with mask designs, highlighting the pivotal role of the fold line in influencing these patterns. This research contributes to the evolving understanding of masks as a fashion statement and provides valuable insights for manufacturers and marketers in the Fashion Mask industry. The results have implications beyond the pandemic, emphasizing the importance of design elements in sustaining consumer interest in face masks.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

• Journal of Oral Medicine and Pain
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• v.33 no.3
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• pp.279-294
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• 2008

This study aimed to make an analysis of the occlusion in the state of muscle fatigue produced by excessive mouth opening and clenching during the dental treatment to control the dental pain and to evaluate the sensory nerve in the muscle pain state. Most of the reasons why patients visit the dental office result in pain-either conceivably the dental origin pain or the non-dental origin pain. The dental offices have many therapeutic actions to produce the masticatory muscle fatigue for the treatment. Dental treatment with long minutes of mouth opening can cause some headaches, masticatory muscle pain and mouth opening difficulties. Patients with mastication problems who visits a dental office to alleviate pain run against another unexpected pain with other aspects. This study uses T-scan II system(Tekscan Co., USA) for the evaluation on the occlusal pattern in the experimental muscle fatigue after clenching, opening the mouth excessively and chewing gum. The occlusal contact pattern is analyzed by the contact timing, namely first, intercuspal, maximum and end point of contact. This inspection was performed at frequencies of 2000Hz, 250 Hz and 5 Hz before and after each experimental muscle pain was produced to 24 subjects who had normal occlusion without the orthodontic treatment or a wide range of the prosthesis by using $neurometer^{\circledR}$ CPT/C(Neurotron, Inc. Baltimore, Maryland, USA). The measuring sites were mandibular nerve experimental muscle fatigue respectively. This study could obtain the following results after the assessment of occlusion and sensory nerve of the experimental muscle fatigue. 1. There were the fastest expression after the excessive mouth opening in muscle fatigue and after tooth clenching in muscle pain. In the visual analog scale that records the subjective level, there was the highest scale after the clenching in the muscle fatigue in jumping off the point of pain. 2. Tooth contact time, contact force, relative contact force on the point of the first contact had no difference, and there were decreases in the contact force after the excessive mouth opening on intercuspal position point, after the excessive mouth opening and the gum chewing on the point of the maximum, and in the contact time after all the experimental muscle fatigue state on the point of the end contact. 3. There was no statistic significance in the current perception threshold before and after the experimental muscle fatigue. 4. There was no significant difference in the contact number, the maximal contact number on the point of the first contact, and the contact number after the mouth opening and gum chewing on the point of the intercuspal position and the contact number after the experimental muscle fatigue on the maximum point, and showed significant decreases. In conclusion, it was found that the occlusal pattern can cause the changes on the case of the clinical muscle weakness by intra-external oral events. It was important that the sedulous attention to details is required during dental treatment in case of excessive mouth opening, mastication and clenching.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.291-298
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• 2007

Purpose: To investigate the feasibility of TI-201 SPECT with intra coronary injection (lC-I) in the detection of viable myocardium, we have performed SPECT imaging after direct intracoronary injection of TI-201 and images were compared with those of stress-reinjection (Re-I) SPECT. Methods: Fourteen coronary artery disease patients (male 11, mean age 54 years) who had myocardial infarction or demonstrated left ventricular wall motion abnormality on echocardiography were enrolled. Three mCi of TI-201 was injected into both coronary arteries during angiography and images were acquired between 6- and 24-hour after injection. Reinjection imaging with 1 mCi of TI-201 was performed at 4-hour after adenosine stress imaging with 3 mCi of TI-201. Images were interpreted according to 4-grade visual scoring system (grade 0-3). Segments with mild to moderated uptake (${\leq}$grade 1), and upgraded more than one score with reinjection, and were defined as viable myocardium. Results: Image quality was poor in two cases with IC-I. Numbers of non-viable segments were 60 (23.8%) with IC-I, and 38 (15.1%) with Re-I, respectively. Overall agreement for perfusion grade per myocardial segment in each IC-I and Re-I was 76.5%. Overall agreement for viable segment between IC-I and Re-I was 90.5%. Only one out of 38 segments interpreted as non-viable with Re-I were interpretated as viable with IC-I. And 23 out of 214 segments interpreted as viable with Re-I were interpreted as non-viable with IC-I. Conclusion: Intracoronary TI-201 SPECT seemed to be not advantageous over stress-rest reinjection imaging in the assessment of myocardial viability, mainly due to low count statistics at 6-hour or 24-hour delayed time points. The feasibility of intracoronary TI- 201 SPECT is considered to be limited.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.