• Journal of the Korean Institute of Landscape Architecture
• /
• v.47 no.3
• /
• pp.1-11
• /
• 2019

The purpose of this study is to propose a sustainable village-unit urban regeneration plan for the Ewha Mural Village, where mural artists recovered concrete fences to be followed by some residents damaging the mural paintings. Through a review of the existing literature and a preliminary survey, we derived the urban regeneration factors (environmental sustainability, economic sustainability, and social sustainability) applicable at the village level. After an empirical survey on the residents, we tried to identify various problems of the Ewha Mural Village. Residents selected the factors of accessibility, parking management, diversity of industries, creation of new jobs, community participation of residents for the mural village's activation, and stable living spaces. In the case of Ewha Mural Village, physical environment factors for the residents at the time of construction were not considered and the village was mainly planned using budget-based murals. Since then, the inequality of economic benefits intensified the conflicts among the residents. In addition, public benefits, such as establishing new industries and employing outsiders, were not provided, and these facts appear to have led to an unsustainable murals village, in which the murals that are the protagonists of the village revitalization are being destroyed. Therefore, the urban regeneration of Ewha Mural Village should be designed considering a region where some residential areas can be transformed into tourist areas. In addition, it is essential to employ a win-win method to improve the living environment, such as road maintenance, not only partial economic benefits, such as increased land-value, and to increase resident's value as a common asset within the village itself.

• The Journal of Korean Academy of Prosthodontics
• /
• v.59 no.3
• /
• pp.261-270
• /
• 2021

Purpose. The aim of this study was (1) to compare the reverse engineering technique with other existing measurement methods and (2) to analyze the effect of implant angulations and impression coping types on implant impression accuracy with reverse engineering technique. Materials and methods. Three different master models were fabricated and the distance between the two implant center points in parallel master model was measured with different three methods; digital caliper measurement (Group DC), optical measuring (Group OM), and reverse engineering technique (Group RE). The 90 experimental models were fabricated with three types of impression copings for the three different implant angulation and the angular and distance error rate were calculated. One-way ANOVA was used for comparison among the evaluation methods (P < .05). The error rates of experimental groups were analyzed by two-way ANOVA (P < .05). Results. While there was significant difference between Group DC and RE (P < .05), Group OM had no significant difference compared with other groups (P > .05). The standard deviations in reverse engineering were much lower than those of digital caliper and optical measurement. Hybrid groups had no significant difference from the pick-up groups in distance error rates (P > .05). Conclusion. The reverse engineering technique demonstrated its potential as an evaluation technique of 3D accuracy of impression techniques.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.175-197
• /
• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of the Korea Institute of Building Construction
• /
• v.20 no.6
• /
• pp.489-495
• /
• 2020

Whereas the control of the hydration heat in mass concrete has been important as the concrete structures enlarge, many conventional strategies show some limitations in their effectiveness and practicality. Therefore, In this study, as a solution of controling the heat of hydration of mass concrete, a method to reduce the heat of hydration by controlling the hardening of cement was examined. The reduction of the hydration heat by the developed Phosphate Inorganic Salt was basically verified in the insulated boxes filled with binder paste or concrete mixture. That is, the effects of the Phosphate Inorganic Salt on the hydration heat, flow or slump, and compressive strength were analyzed in binary and ternary blended cement which is generally used for low heat. As a result, the internal maximum temperature rise induced by the hydration heat was decreased by 9.5~10.6% and 10.1~11.7% for binder paste and concrete mixed with the Phosphate Inorganic Salt, respectively. Besides, the delay of the time corresponding to the peak temperature was apparently observed, which is beneficial to the emission of the internal hydration heat in real structures. The Phosphate Inorganic Salt that was developed and verified by a series of the aforementioned experiments showed better performance than the existing ones in terms of the control of the hydration heat and other performance. It can be used for the purpose of hydration heat of mass concrete in the future.

• Korean Journal of Comparative Education
• /
• v.24 no.3
• /
• pp.127-156
• /
• 2014

This study was inspired by the issue that the fundamentals of education have been overlooked, as today's smart education policies established in the knowledge-based information society of the 21st century have only focused on building digital environment and its efficiency. To carry out the study, the media education of Germany, which is equivalent of Korea's smart education, was analyzed to obtain implications for Korea's smart education. In Germany, the media education has been managed by the country ever since the information society has begun. Since 2009, the media education has become a requirement for all schools in every state. Thus, the current media education policy of each state has been analyzed, which revealed the following common characteristics. 1) The media education is closely linked to existing curriculum and education, rather than being conducted separately with different standards. 2) The media education is being conducted in a democratic manner by actively reflecting the exemplary cases of school teachers, rather than following the instructions and guidelines from the government. 3) The media education deals with the character and identity of young students, based on their basic understanding of information society, which are essential for a successful life in the upcoming society. Unlike the first and second implication linked to the method and procedure of media education policy, the third implication is the basic purpose of media education, which is also the key implication of this study. The media education policy of Germany, which is being conducted with its own educational philosophy, offers significant implications for Korea's smart education policy. In Korea, the education only revolves around device-based environment innovation or content development. It should be noted that the purpose of smart education is developing smart individuals who can bring better, happier, and more successful society - rather than establishing a smart environment. Therefore, the focus of discussion on Korea's smart education that revolves around environment, infrastructure, device utilization, and contents development should be changed to the character and identity of students, which are required in the future smart era. That's when 'human-based' educational revolution, instead of 'device-based' classroom revolution can begin.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.3
• /
• pp.13-29
• /
• 2021

The purpose of this study was to analyze the spatial distribution and location factors of startups by generation (young and middle-aged) in Seoul. To this end, a research model was established that included factors of industry, population, and startup institutions by generation in 424 administrative districts using the Seoul Business Enterprise Survey(2018), which includes data on the age group of entrepreneurs. As an analysis method, descriptive statistics were conducted to confirm the frequency, average and standard deviation of startups by generation and major variables in the administrative districts of Seoul, and spatial distribution and characteristics of startups by generation were analyzed through global and local spatial autocorrelation analysis. In particular, the spatial distribution of startups in Seoul was confirmed in-depth by categorizing and analyzing startups by major industries. Afterwards, an appropriate spatial regression analysis model was selected through the Lagrange test, and based on this, the location factors affecting startups by generation were analyzed. The main results derived from the research results are as follows. First, there was a significant difference in the spatial distribution of young and middle-aged startups. The young people started to startups in the belt-shaped area that connects Seocho·Gangnam-Yongsan-Mapo-Gangseo, while middle-aged people were relatively active in the southeastern region represented by Seocho, Gangnam, Songpa, and Gangdong. Second, startups by generation in Seoul showed various spatial distributions according to the type of business. In the knowledge high-tech industries(ICT, professional services) in common, Seocho, Gangnam, Mapo, Guro, and Geumcheon were the centers, and the manufacturing industry was focused on existing clusters. On the other hand, in the case of the life service industry, young people were active in startups near universities and cultural centers, while middle-aged people were concentrated on new towns. Third, there was a difference in factors that influenced the startup location of each generation in Seoul. For young people, high-tech industries, universities, cultural capital, and densely populated areas were significant factors for startup, and for middle-aged people, professional service areas, low average age, and the level of concentration of start-up support institutions had a significant influence on startup. Also, these location factors had different influences for each industry. The implications suggested through the study are as follows. First, it is necessary to support systematic startups considering the characteristics of each region, industry, and generation in Seoul. As there are significant differences in startup regions and industries by generation, it is necessary to strengthen a customized startup support system that takes into account these regional and industrial characteristics. Second, in terms of research methods, a follow-up study is needed that comprehensively considers culture and finance at the large districts(Gu) level through data accumulation.

• Journal of the Korean Wood Science and Technology
• /
• v.49 no.4
• /
• pp.336-359
• /
• 2021

In this study, hybrid CLT research and development trends were analyzed to improve the low rolling shear strength of CLT, a large wooden panel used in high-rise wooden buildings. Through this, basic data that can be used in research and development directions for localization of CLT were prepared. As a way to improve the low rolling shear strength, the use of hardwood lamina, the change of the lamina arrangement angle, and the use of structural composite materials are mainly used. Rolling shear strength and shear modulus of hardwood lamina are more than twice as high as softwood lamina. It confirmed that hardwoods can be used and unused species can be used. Rolling shear strength 1.5 times, shear modulus 8.3 times, bending stiffness 4.1 times improved according to the change of the layer arrangement angle, and the CLT strength was confirmed by reducing the layer arrangement angle. Structural wood-based materials have been improved by up to 1.35 times MOR, 1.5 times MOE, and 1.59 times rolling shear strength when used as laminas. Block shear strength between the layer materials was also secured by 7.0 N/mm², which is the standard for block shear strength. Through the results of previous studies, it was confirmed that the strength performance was improved when a structural wood based materials having a flexural performance of MOE 7.0 GPa and MOR 40.0 MPa or more was used. This was determined based on the strength of layered materials in structural wood-based materials. The optimal method for improving rolling shear strength is judged to be the most advantageous application of structural wood based materials with strength values according to existing specifications. However, additional research is needed on the orientation of CLT lamina arrangement according to the fiber arrangement of structural wood-based materials, and the block shear strength between lamina materials.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.23-31
• /
• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.177-190
• /
• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Clean Technology
• /
• v.27 no.1
• /
• pp.1-8
• /
• 2021

Due to the negative impacts of microplastics (MPs) on the ecosystem, the investigation of its occurrence and its treatment from sewage and wastewater treatment plants (WWTPs) have received a lot of attention in the recent years. Most MPs are precipitated and removed with the sludge during the treatment process. Proper sludge management is immensely necessary to avoid MP exposure in the environment. However, the domestic research on this aspect is limited. This study reviews appropriate sludge management approaches to decrease environmental MP exposure. This can be achieved through investigating sludge generation and treatment, regulation laws and government policy trends with an emphasis on WWTPs. The ratio of sludge in sewage treatment plants has been observed to be highest in recycling followed by incineration and landfills. Recycling is the highest in fuel followed by construction materials and composting. For WWTPs, the highest ratio is in recycling followed by fuel and landfills, and recycling is confirmed in the following order: incineration > after composting > after solidification > earthworm breeding. Treatment approaches that can increase the exposure of MPs to the ecosystem are considered to be used in landfills and agricultural fields. However, this method is not appropriate given the insufficient capacity of domestic landfills and the sufficient supply of existing chemical and animal manure fertilizers. Instead, it would be rational in terms of environmental preservation to expand the use of fuel and energy in connection with the new and renewable energy policy, and to actively seek the use of sub-materials for construction materials. In order to secure the basic data for the effectiveness of future planning and revision of related laws, it is required to perform an in-depth investigation of the sludge supply and demand status along with the environmental and economic effects.