• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• Journal of the Korean Wood Science and Technology
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• v.44 no.5
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• pp.655-663
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• 2016

Increasing green house gas and it consequent climate change problems are discussed as a global issue. Accordingly, future local green house gas emission will increase up to 40% of the entire local green house gas emission and therefore, efforts to reduce the emission in construction industry is urgently required. Therefore, in this study, heating energy demand was analyzed by using the EnergyPlus simulation according to wood material finishes configuration. EnergyPlus has the entry for a variety of buildings and heating, ventilation, air conditioning (HAVC) system components, in particular buildings, air conditioning systems, and performs simultaneous integrated calculated through the feedback between the heat source unit, a verification program according to the ASHRAE Standard 140-2007 to be. The climate data for the simulation we used the data IWEC in Incheon and Gwangju provided by EnergyPlus. The analysis of simulation model was farm and fishing house standard design drawings: 2012, presented at the Korea Rural Community Corporation. The results of simulation of central region and southern region were effected by wood products of simulation model into the interior finish, exterior finish, windows, wooden structure. Also, it was confirmed that the reduced heating energy demand.

• Ocean and Polar Research
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• v.30 no.4
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• pp.453-466
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• 2008

In order to examine the generation mechanism of long ocean waves along the west coast of Korea and to understand the amplification process of the long ocean waves, sea level, atmospheric pressure and wind data observed every minute from 2007 March 29 to 2007 April 1 were analyzed and onedimensional numerical ocean model experiments were performed. An atmospheric pressure jump propagated southeastward from Backryungdo to Yeonggwang along the west coast of Korea with speed of $13{\sim}27\;m/s$ between 2007 March 30 23:00 and 2007 April 1 1:30. Average magnitude of pressure jump was 4.2 hPa. As a moving atmospheric jump propagated from north to south along the coast, long ocean waves were generated and the sea level abnormally rose or fell at Anheung, Kunsan, Wido and Yeonggwang. Average amplitude of sea level rise (or fall) was about 113.6 cm. In a one-dimensional numerical ocean model, nonlinear shallow water equations were numerically integrated and a moving atmospheric pressure jump with traveling speed of 24 m/s was used as an external force. While the atmospheric pressure jump travels over 60 m depth ocean, a long ocean wave is generated. Because the propagation speed of the atmospheric jump is almost equal to that of the long ocean wave, Proudman resonance occurs and the long ocean wave amplifies. As the atmospheric pressure jump moves into the coastal area shallower than 60 m, the speed of the long ocean wave decreases and Proudman resonance effect decreases. However, the amplitude of the long ocean wave increases and wave length becomes shorter because of shoaling effect. When the long ocean wave hits the land boundary, amplitude of the long ocean wave drastically amplifies due to reflection. Data analysis and numerical experiments suggest that the southeastward propagation of an atmospheric pressure jump over the shallow ocean, which is a necessary condition for Proudaman resonance, generated the long ocean waves along the west coast of Korea on 2007 March 31 and the ocean waves amplified due to shoaling effect in the coastal area and reflection at the shore.

• The Korean Journal of Franchise Management
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• v.10 no.4
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• pp.19-29
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• 2019

Purpose: In beauty industry, service quality is very critical, because it impacts on the customer's positive attitude and behavior to the beauty salon or beauty brand. Thus, this research examines the effects of service attributes on customer satisfaction and loyalty in beauty salon. This research suggests the guidelines for how beauty salons should manage their physical environment, price policy, professional skills, and employees that improve management and business performance. Research design, data, and methodology: This study examines the structural relationship between service attributes, customer satisfaction, and loyalty. Service attributes divide into four sub-dimensions such as servicescape, price service, technical service, and employee service. In order to test the purposes of this research, research model and hypotheses were developed. All constructs were measured with multiple items developed and examined in previous studies. A total of 160 questionnaires were distributed and collected, and 150 were used for analysis except 10 that were unresponsive or unfaithful. The data were analyzed using SPSS 22.0 and SmartPLS 3.0 statistical package program. Result: The results of this research are as follows. First, all sub-dimensions of service attributes such as servicescape, price service, technical service, and employee service have significant positive impacts on satisfaction. Second, customer satisfaction have significant impact on loyalty. Conclusions: This study suggests an integrated model of the relationship that the characteristics of beauty salon service attributes affect customer loyalty through satisfaction, and suggests how to manage and allocate limited resources in the beauty industry. The findings of this research indicate that the level of customer satisfaction is shown to be increased by servicescape, technical characteristics, value of money, and human attributes. Thus, beauty salon management should focus on the relationship with their customers how to improve customer loyalty through satisfaction. The quality of beauty service influences customer's attitudes and behaviors toward beauty salon. Considering the beauty business, where the quality and customer satisfaction of beauty services are determined by the hairdresser's beauty skills,, the beauty salons must find ways to improve their skills and new trend of hair style. If beauty salon customers perceive the high quality of beauty service, they revisit beauty salon and recommend it to others.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.685-693
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• 2021

This study examined the fire risk of the region in Goyang City using the spatial information data of buildings. The economic damage by industry was assessed according to the probability of fire risk. The study area was confined to Goyang-si, Gyeonggi-do, and the same fire risk reduction rate was applied to each region for the convenience of analysis. The possibility of fire was derived based on the buildings' density and usage in the area by National GIS building-integrated information standard data. The calculation of economic damage by industry in Goyang City due to the fire risk was calculated by combining the Goyang-si industry-related model produced by matching with 30 industrial categories in Input-Output Statistics of Korea Bank and 20 industrial categories in the Goyang-si business survey and the possibility of fire. The basic scenario of production impossibility during six months and business loss due to fire was established and analyzed based on the supply model. The analysis showed that Ilsan-dong-gu, Ilsan-seo-gu, and Deokyang-gu suffered the most economic damage. The "electricity, gas, steam, and water business" showed the greatest loss by industry.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.99-113
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• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1135-1144
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• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.