• Archives of design research
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• v.18 no.1 s.59
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• pp.195-204
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• 2005

Due to the of evolution of digital technology, trends are moving toward personalization and customization in design (art), media, science. Existing mass media has been broadcasting to the general public due to technical and economic limitation and art works also communicate one-sidedly with spectators in the gallery or stage. But nowaday, it is possible for spectators to participate directly. We can make different products depending on the tastes of individuals who demand media or art. The essence of technology which makes it possible is 'interactive technology'. A goal of this research is to find out the true nature of the interactive design in multimedia contents and find the course of interactive communication design research. In this paper, we pass through two stages to solve this kind of problem. At first, we studied the concept of multimedia contents from the aspect of information revolution. Next, we decided our research topic to be 'visual reacting with audio' and made audio-visual art work as graphic designers. Through this research we can find the possibility to promote 'communication' in a broad sense, with appropriate interactive design.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.4
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• pp.175-189
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• 2017

Science and technologies develop rapidly and in conditions where it is difficult to predict both domestically and abroad, the rapidly changing business environment demands innovation. To strengthen the businesses' competitive edge, the key challenge is not to control but rather grant autonomy, allowing it to anticipate the future and actively deal with. This will allow to train and manage talented people with competence and abilities, which all links to the concept of the Super Leadership. Currently, our society is in the change due to the Fourth Industrial Revolution wave and paradigm related with high-tech industry, hence is in desperate need for creativeness and speed management. It is a time where risk and challenges coexist and is not a standardized environment. Businesses must make efforts in managing human resources and allow them to have leaderships compatible with the current era, giving them opportunities to face challenges. The necessity for Super Leadership is emphasized especially in the industry-orientated era, where new revolutionary technologies are emerging and existing technologies are being advanced and maximized, to cultivate the potentials of the members and secure competitive superiority for the best management and creation. Therefore, in this research, the empirical analysis of the relationship between Super Leadership of small and medium enterprises and the business outcome was conducted. Research outcome states that, in order to create a sustainable business outcome, enterprises need to invest more in developing leaderships and in accordance with it, promote talent. In order to survive and sustain growth in the period of endless competition like today, it is concluded that it is imperative to establish a powerful Super Leadership that will endeavor to engage and maximize the competence of members of the organization.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.6
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• pp.346-353
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• 2002

The explosive increase of World Wide Web usage has incurred significant amount of network traffic and server load. To overcome these problems, web proxy caching replicates frequently requested documents in the web proxy closer to the users. Cache utilization depends on the replacement policy which tries to store frequently requested documents in near future. Temporal locality and Zipf frequency distribution, which are commonly observed in web proxy workloads, are considered as the important properties to predict the popularity of documents. In this paper, we propose a novel cache replacement policy, called Adaptive LFU (ALFU), which incorporates 1) Zipf frequency distribution by utilizing LFU and 2) temporal locality adaptively by measuring the amount of the popularity reduction of documents as time passed efficiently. We evaluate the performance of ALFU by comparing it to other policies via trace-driven simulation. Experimental results show that ALFU outperforms other policies.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.453-462
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• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.637-645
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• 2019

Due to the transformation to the intelligent information society, the rapid change of our life and environment is expected. The Ministry of Science and ICT (MSIT) and the National Research Council of Science and Technology (NST) introduced a five-year government supported research institution's planning and evaluation based on the mid-to long-term perspective. This study collects international benchmarking information including industry, academia, and research fields by collecting mid- and long-term strategy reports from public research institutes, surveys by experts from abroad universities and research institutes, and analyzing overseas market information reports. The British Geological Survey (BGS), the U.S. Geological Survey (USGS) and the japanese geological survey related institutes (AIST-GSJ) plans for three-dimensional national geological information, predictions of geological environmental disasters, and development of important metals and material in the low carbon economic transformation and in the era of the Fourth Industrial Revolution. The mid- and long-term program emphasizes basic and public research on geological information through abroad experts survey such as the IPGP-CNRS etc. The market analysis of the mining automation and digital map sectors has been able to derive the fields in which the role of public research institutes by the market is expected such as data collection on land and in the air, mobile or three-dimensional information production, smooth/fast/real-time maps, custom map design, mapping support to various platforms, geological environmental risk assessment and disaster management information and maps.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.329-335
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• 2019

Reliability growth analysis was conducted for a guided weapons system. In the development phase, reliability management activities were continuously carried out by identifying failure modes and causes and analyzing faults found during the testing. The missile system consists of an all-up-round missile and a launcher, and the analysis was carried out according to the test results of each system. The test results for the all-up-round missile were obtained with discrete data, which were success and failure as a one-shot-device. The test results for the launcher were obtained with continuous data by operating the equipment continuously in the test. For each test result, the reliability growth model was applied to the Standard Gompertz model and the Crow-Extended model. The models were used to identify the growth analysis results of the test so far. It was also possible to predict the reliability growth results by assuming the future test results. The study results could be useful in achieving the desired reliability goal and in determining the number of tests. Then, the planned test will be confirmed and the growth analysis of the missile system will continuously be conducted.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.2
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• pp.179-186
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• 2014

Metabolomics is the study of changes in the metabolic status of an organism as a consequence of drug treatment, environmental influences, nutrition, lifestyle, genetic variations, toxic exposure, disease, stress, etc, through global or comprehensive identification and quantification of every single metabolite in a biological system. Since most chronic diseases have been demonstrated to be linked to nutrition, nutritional metabolomics has great potential for improving our understanding of the relationship between disease and nutritional status, nutrient, or diet intake by exploring the metabolic effects of a specific food challenge in a more global manner, and improving individual health. In particular, metabolite profiling of biofluids, such as blood, urine, or feces, together with multivariate statistical analysis provides an effective strategy for monitoring human metabolic responses to dietary interventions and lifestyle habits. Therefore, studies of nutritional metabolomics have recently been performed to investigate nutrition-related metabolic pathways and biomarkers, along with their interactions with several diseases, based on animal-, individual-, and population-based criteria with the goal of achieving personalized health care in the future. This article introduces analytical technologies and their application to determination of nutritional phenotypes and nutrition-related diseases in nutritional metabolomics.