• Korean Journal of Community Nutrition
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• v.26 no.5
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• pp.327-336
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• 2021

Objectives: This study was conducted to create a 3D printable snack dish model for the elderly with low food or fluid intake along with barriers towards eating. Methods: The decision was made by the hybrid-brainstorming method for creating the 3D model. Experts were assigned based on their professional areas such as clinical nutrition, food hygiene and chemical safety for the creation process. After serial feedback processes, the grape shape was suggested as the final model. After various concept sketching and making clay models, 3D-printing technology was applied to produce a prototype. Results: 3D design modeling process was conducted by SolidWorks program. After considering Dietary reference intakes for Koreans (KDRIs) and other survey data, appropriate supplementary water serving volume was decided as 285 mL which meets 30% of Adequate intake. To consider printing output conditions, this model has six grapes in one bunch with a safety lid. The FDM printer and PLA filaments were used for food hygiene and safety. To stimulate cognitive functions and interests of eating, numbers one to six was engraved on the lid of the final 3D model. Conclusions: The newly-developed 3D model was designed to increase intakes of nutrients and water in the elderly with dementia during snack time. Since dementia patients often forget to eat, engraving numbers on the grapes was conducted to stimulate cognitive function related to the swallowing and chewing process. We suggest that investigations on the types of foods or fluids are needed in the developed 3D model snack dish for future studies.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.265-271
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• 2021

The pattern of the tree's internal swelling depends on many causes. Since it is difficult to detect these various causes of swelling with a general method, if the state of swelling for a long time cannot be confirmed, serious damage to the trees may occur due to enlargement of the swelling area. In the method of acquiring a tree tomography image, an impulse passing through the tree is generated by tapping the sensor with a rubber mallet, and the moving speed is recorded. In this paper, to measure cracks, cavities, and swelling due to physical damage, we developed a 3D viewer that can know the internal state of a tree using a tree cross-section image acquired from Arbotom to determine the degree of swelling inside the tree. Based on this, we tried to present data that can be referred to when surgical operation of trees is required. In order to acquire a tomographic image of a tree, 6 sensors were attached to the three Yangpala and Maple trees, and a 1 m-long tree was measured using the Arbotom program, and a 3D image was implemented through the 3D Viewer created using MATLAB. In addition to simply acquiring images, the cross-sectional length and volume of the tree were measured. In the actually produced 3D Viewer, the length of the part where the swelling of the maple tree occurred was 33.12 cm, and the swelling of the yangpala tree was measured as 21.41 cm. The volume of the maple tree was measured to be 78.832 ㎤. As a result of comparing the cross-sectional image of the Arbotom and the 3D image, the same result as the real aspect of the tree was obtained, so it can be judged that the reliability of the manufactured software is also secured, and data to be applied to the surgical tree operation through the created Viewer is provided. It is believed that the damage will be minimized.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of Korean Philosophical Society
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• v.145
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• pp.91-117
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• 2018

This paper aims to clarify the similarities and differences between the Zhouyi and artificial intelligence. The divination of the Zhouyi is rooted in the oldest system of human knowledge, while artificial intelligence stands at the cutting edge of modern scientific revolution. At first sight, there does not appear to be any association that links the one to the another. However, they share the same ground as seen from a semiotic standpoint because both of them depend on the semiotic system as a means of obtaining knowledge. At least four aspects can be pointed out in terms of similarities. First, artificial intelligence and the Zhouyi use artificial language that consists of semiotic signs. Secondly, the principle that enables divination and artificial intelligence lies in imitation and representation. Thirdly, artificial intelligence and the Zhouyi carry out inferences based on mathematical algorithms that adopt the binary system. Fourth, artificial intelligence and the Zhouyi use analogy as a means of obtaining knowledge. However, those similarities do not guarantee that the Zhouyi could arrive at the scientific certainty. Nevertheless, it can give us important insight into the essence of our civilization. The Zhouyi uses intellect in order to get new information about the unknown world. However, it is hard to know what kind of intellect is involved in the process of divination. Likewise, we do not know the fundamental character of artificial intelligence. The intellect hidden in the unknown subject is a mystic and fearful existence to us. Just as the divination of the Zhouyi inspires the sense of reverence toward the supernatural subject, we could not but have fear in front of the invisible subject hidden in artificial intelligence. In the past, traditional philosophy acknowledged the existence of intellect only in conscious beings. Nonetheless, it becomes evident that human civilization ushers into a new epoch. As Ray Kurzweil mentioned, the moment of singularity comes when artificial intelligence surpasses human intelligence. In my viewpoint, the term of singularity can be used for denoting the critical point in which the human species enters into the new phase of civilization. To borrow the term of Shao Yong(邵雍) in the Northern Song Dynasty, the past civilization belongs to the Earlier Heaven(先天), the future civilization belongs to the Later Heaven(後天). Once our civilization passes over the critical point, it is impossible to go back into the past. The opening of the Later Heaven foretold by the religious thinkers in the late period of Joseon Dynasty was a prophecy in its own age, but it is becoming a reality in the present.

• 한국사회정책
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• v.25 no.2
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• pp.429-446
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• 2018

The $4^{th}$ Industrial Revolution, symbolizing the explosive innovation of digital technologies, is expected to have a great impact on labor relations and produce a lot of contested issues. The labor-management issues, created by the $4^{th}$ Industrial Revolution, are as follows: (1) employment restructuring, job re-allocation, and skill-reformation, driven by the technological displacement, resetting of worker-machine relationship, and negotiation on labor intensity and autonomy, (2) the legislation of institutional protection for the digital dependent self-employed, derived from the proliferation of platform-mediated labor, and the statutory recognition of their 'workerness', (3) unemployment safety net, income guarantee, and skill formation assistance for precarious workeforce, (4) the protection of worker privacy from workplace surveillance, (5) protecting labor rights of the digital dependent self-employed and prcarious workers and guaranteeing their unionization and collective bargaining. In comparing how labor unions in Western countries have responded to the $4^{th}$ Industrial Revolution, German unions have showed a strategic approach of policy formation toward digital technological innovations by effectively building and utilizing diverse channel of social dialogue and collective bargaining, while those in the US and UK have adopted the traditional approach of organizing and protesting in attempting to protect the interest of platform-mediated workers (i.e. Uber drivers). In light of the best practice demonstrated by German unions, it is necessary to build the process of productive policy consultation among three parties- the government, employers, and labor unions - at multi levels (i.e. workplace, sectoral and national levels), in order to prevent the destructive damage as well as labor-management confrotation, caused by digital technological innovations. In such policy consultation procesess, moreover, the inclusive and integrated approach is required to tackle with diverse problems, derived from the $4^{th}$ Industrial Revolution, in a holistic manner.

• Journal of vocational education research
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• v.36 no.1
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• pp.47-75
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• 2017

The purpose of this study was to provide basic data for policy making for secondary vocational education in each region and transformation in vocational high schools. To achieve this, the regional strategic industries in Daejeon and Jeonnam were selected, new demand for vocational high school graduates was forecasted in each industry and occupation. The results of the study are as follows. First, locational quotient analysis and regional shift-share analysis revealed that Daejon and Jeonnam have different strategic industries. Daejon, unlike Jeonnam strategically develops 'manufacturing food, beverage and tobacco', 'manufacturing timber and paper, printing and copying', 'public service and administration of national defense and social security' and 'manufacturing electrical devices, electronics and precision devices'. Jeonnam has specialized industries distinguished from Daejon's, which are 'manufacturing of machinery transportation equipments and etc', 'manufacturing of non-metallic minerals and metal products', 'electric, gas, steam and water supply systems/industries', 'manufacturing coal and chemical products, refining petroleum', 'mining' and 'agriculture, forestry and fishery'. Second, new demand for vocational high school graduates by occupations and industries showed regional differences(in Daejon and Jeonnam). According the forecast, Daejon will have many workforce demands based on manufacturing industries, on the other hand Jeonnam's focused on service industries. Analysis by occupations was also different, Daejon showed high demands on professional and related workers, while Jeonnam requested many new office and service workers. Third, new workforce demand by occupations in regional strategic industries is big part of overall new workforce demand both in Daejon and Jeonnam. Forth, according to the results of analyzing the new demand for vocational high school graduates in Daejeon and Jeonnam in terms of industry location quotient and change effect, there was high demand in industries with positive total change effects. In terms of location quotient, Daejeon and Jeonnam showed different results.

• Food Engineering Progress
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• v.15 no.1
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• pp.48-55
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• 2011

In this research, the near-infrared absorption from 1,100-2,300 nm was used to measure the content of capsaicinoids in the red-pepper powder by using the Acousto-optic tunable filters (AOTF) spectrometer with sample plate and sample rotating unit. Non-spicy red-pepper samples from one location (Younggwang-gun. Korea) were mixed with spicy one (var. Chungyang) to make samples separated by particle size (below 0.425 mm, 0.425-0.71 mm, and 0.71- 1.4 mm). The Partial Least Squares Regression (PLSR) model to predict the capsaicinoid content on particle sizes was developed with measured spectra by AOTF spectrometer and used to analyze the amount of capsaicinoids by HPLC. The PLSR Model of red-pepper powder of below 0.425 mm, 0.425-0.71 mm, and 0.71-1.4 mm with cross validation had ${R_V}^2$ = 0.948-0.979 and Standard Error of Prediction (SEP) = 6.56-7.94 mg%. The prediction error of smaller particle size of red-pepper powder was low. The best PLSR model was found in pretreatment of Range Normalization, Standard Normal Variate, and 1st Derivatives of red-pepper powder of below 1.4 mm with cross validation, having ${R_V}^2$ = 0.959 and SEP = 8.82 mg%.