• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.207-236
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• 2013

This article aims to understand the process of effective leadership in the Community Care Centers for the Disabled(CCCD). Using a qualitative approach and the concept of 'Sensemaking', a term introduced by Weick (1995), I described and explored the process of leadership. Sensemaking enables leaders to have a better grasp of what is going on in their environments. Sensemaking theory offers a perspective on leadership that resolves successfully complex situations surrounding the CCCD. The CCCDs face the environmental changes such as a paradigm shift of disability and a rivalry system with private services. In this time of uncertain change, the CCCDs are in need of leadership of the executives who could reduce complexity with shared meaning. Data were collected over 5 weeks from the 6 CCCDs, through semi-structured interviews, with practitioners, middle managers and chief executives. The interview scripts were thematically analyzed through Atlas-ti programme. The findings showed three subjects, the people's perception of environment, organizational visioning and interactions among post positions. Even though the 6 CCCDs were under the same environment, the perceptions and the enactments of the practitioners were influenced by the chief executives' sensemaking. The important factors of the chief executives' sensemaking were the daily interaction as well as ongoing reflection on their experiences.

• Journal of Korea Water Resources Association
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• v.52 no.spc2
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• pp.765-776
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• 2019

The purpose of this study is to provide the legal and institutional guidelines and standards that can be used in the whole river restoration project and to analyze and evaluate the performance of the river project. We constructed an assessment system of four biological taxa that can represent the river environments, namely, evaluation indexes and standards of vegetation and birds, benthic invertebrates and fishes. Specifically, the assessment indicator and criteria of biological characteristics are summarized, so that in case of vegetation community, vegetation diversity, vegetation complexity, and vegetation naturalness can be quantitatively assessed through the combination of three indices. Based on the scientific basis of the advanced techniques, benthic invertebrates, fishes, and birds were proposed to quantitatively evaluate assessment grades according to the classification of biological data. In order to evaluate biological characteristics, which are a part of river environmental naturalness, we proposed a comprehensive biological index and evaluation grade applying the weight of these four biological taxa, and it clearly reflects the characteristics of river environment in test bed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.846-855
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• 2021

Eco-friendly and renewable energy sources are actively being researched in recent times, and of shore wind power generation requires advanced design technologies in terms of increasing the capacities of wind turbines and enlarging wind turbine installation vessels (WTIVs). The WTIV ensures that the hull is situated at a height that is not affected by waves. The most important part of the WTIV is the leg structure, which must respond dynamically according to the wave, current, and wind loads. In particular, the wave load is composed of irregular waves, and it is important to know the exact dynamic response. The dynamic response analysis uses a single degree of freedom (SDOF) method, which is a simplified approach, but it is limited owing to the consideration of random waves. Therefore, in industrial practice, the time-domain analysis of random waves is based on the multi degree of freedom (MDOF) method. Although the MDOF method provides high-precision results, its data convergence is sensitive and difficult to apply owing to design complexity. Therefore, a dynamic amplification factor (DAF) estimation formula is developed in this study to express the dynamic response characteristics of random waves through time-domain analysis based on different variables. It is confirmed that the calculation time can be shortened and accuracy enhanced compared to existing MDOF methods. The developed formula will be used in the initial design of WTIVs and similar structures.

• Smart Media Journal
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• v.10 no.2
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• pp.61-69
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• 2021

Wayfinding is a 'Process solving problem to find destination', and it is important to select spatial data for optimal way. Recently, due to the complexity of space and the expansion of the medium of the wayfinding service, it is necessary to the approach on the information design for them. Therefore, the purpose of this study is to propose a meta guideline on the information design for the design of orientation sign, which is an important cognitive clue in the wayfinding. It was conducted in 3 stages, First, a design process was proposed and design elements were derived for each step by literature research related to information and sign design, and analysis of manual for signage design. Second, a meta guideline for information organization and visualization in the three-stage design process was proposed by FGI and analysis. Third, the meta guideline was applied to the sign design on an area for user evaluation to inspect the applicability of the meta guideline. Through the user questionnaire, the possibility of applying the guideline for visualization of directions and spaces, information hierarchy according to spatial characteristics and information priority was identified. It is meaningful in that necessary element for signage design are systematized centering on the process and that it presents a macroscopic methodology according to spatial characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.55-63
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• 2022

In recent years, with the rapid development of large and medium-sized urban rail transit in China, the total operating mileage of high-speed railway and the total number of EMUs(Electric Multiple Units) are rising. The system complexity of high-speed EMU is constantly increasing, which puts forward higher requirements for the safety of equipment and the efficiency of maintenance.At present, the maintenance mode of high-speed EMU in China still adopts the post maintenance method based on planned maintenance and fault maintenance, which leads to insufficient or excessive maintenance, reduces the efficiency of equipment fault handling, and increases the maintenance cost. Based on the intelligent operation and maintenance technology of PHM(prognostics and health management). This thesis builds an integrated PHM platform of "vehicle system-communication system-ground system" by integrating multi-source heterogeneous data of different scenarios of high-speed EMU, and combines the equipment fault mechanism with artificial intelligence algorithms to build a fault prediction model for traction motors of high-speed EMU.Reliable fault prediction and accurate maintenance shall be carried out in advance to ensure safe and efficient operation of high-speed EMU.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.88-99
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• 2021

In this study, a land-based marine closed mesocosm (LMCM) experiment was performed to objectively assess the initial stability of an artificial ecosystem experiment against biological and non-biological factors when evaluating ecosystem risk assessment. Changes in the CV (coefficient of value) amplitude were used as data to analyze the stability of the experimental system. The CV of the experimental variables in the LMCM groups (200, 400, 600, and 1,000 L) was maintained within the range of 20-30% for the abiotic variables in this study. However, the difference in CV amplitude in biological factors such as chlorophyll-a, phytoplankton, and zooplankton was high in the 600 L and 1,000 L LMCM groups. This result was interpreted as occurring due to the lack of control over biological variables at the beginning of the experiment. In addition, according to the ANOVA results, significant differences were found in biological contents such as COD (chemical oxygen demand), chlorophyll-a, phosphate, and zooplankton in the CV values between the LMCM groups(p<0.05). In this study, the stabilization of biological variables was necessary to to control and maintain the rate of changes in initial biological variables except for controllable water quality and nutrients. However, given the complexity of the eco-physiological activities of large-scale LMCMs and organisms in the experimental group, it was difficult to do. In conclusion, artificial ecosystem experiments as a scientific tool can distinguish biological and non-biological factors and compare and analyze clear endpoints. Therefore, it is deemed necessary to establish research objectives, select content that can maintain stability, and introduce standardized analysis techniques that can objectively interpret the experimental results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.268-279
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• 2021

The geospatial schemes based on topo-climatology have been developed to produce digital climate maps at a site-specific scale. Their development processes are reviewed here to derive the needs for new schemes in the future. Agricultural and forestry villages in Korea are characterized by complexity and diversity in topography, which results in considerably large spatial variations in weather and climate over a small area. Hence, the data collected at a mesoscale through the Automated Synoptic Observing System (ASOS) operated by the Korea Meteorological Administration (KMA) are of limited use. The geospatial schemes have been developed to estimate climate conditions at a local scale, e.g., 30 m, lowering the barriers to deal with the processes associated with production in agricultural and forestry industries. Rapid enhancement of computing technologies allows for near real-time production of climate information at a high-resolution even in small catchment areas and the application to future climate change scenarios. Recent establishment of the early warning service for agricultural weather disasters can provide growth progress and disaster forecasts for cultivated crops on a farm basis. The early warning system is being expanded worldwide, requiring further advancement in geospatial schemes and digital climate mapping.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.