• International journal of advanced smart convergence
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• v.9 no.2
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• pp.8-19
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• 2020

We see an energy fucused smart city evolution of the UK along with the project of "Smart London Plan (SLP)." A theoretical logic of business model innovation has been discussed and a research framework of evolving energy focused smart city is formulated. The starting point is the silo system. In the second stage, the private investment in smart meters establishes a basement for next stages. As results, the UK's smart energy sector has evolved from smart meter installation through smart grid to new business models such as water-energy nexus and microgrid. Before smart meter installation of the government, the electricity system was centralized. However, after consumer engagement plan has been set to make them understand benefits that they can secure through smart meters, the customer behavior has been changed. The data analytics firm enables greater understanding of consumer behavior and it helps energy industry to be smart via controlling, securing and using that data to improve the energy system. In the third stage, distribution network operators (DNOs)' access to smart meter data has been allowed and the segmentation starts. In the fourth stage, with collaboration of Ofwat and Ofgem, it is possible to eliminate unnecessary duplication of works and reduce interest conflict between water and electricity. In the fifth stage, smart meter and grid has been integrated as an "adaptive" system and a transition from DNO to DSO is accomplished for the integrated operation. Microgrid is a prototype for an "adaptive" smart grid. Previous steps enable London to accomplish a platform leadership to support the increasing electrification of the heating and transport sector and smart home.

• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.81-87
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• 2012

The current defacing method for keeping an anonymity of brain images damages the integrity of a precise brain analysis due to over removal, although it maintains the patients' privacy. A novel method has been developed to create an anonymous face model while keeping the voxel values of an image exactly the same as that of the original one. The method contains two steps: construction of a mockup brain template from ten normalized brain images and a substitution of the mockup brain to the brain image. A level set segmentation algorithm is applied to segment a scalp-skull apart from the whole brain volume. The segmented mockup brain is coregistered and normalized to the subject brain image to create an anonymous face model. The validity of this modification is tested through comparing the intensity of voxels inside a brain area from the mockup brain with the original brain image. The result shows that the intensity of voxels inside from the mockup brain is same as ones from an original brain image, while its anonymity is guaranteed.

• Journal of Global Scholars of Marketing Science
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• v.16 no.2
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• pp.77-94
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• 2006

The purpose of this study was to identify the consumers who use the level of price as the indicator of the product quality and calibrate the price response function with price-dependent quality evaluation. In order to implement the purpose of this study, Home theater market in China had been segmented by the mixture regression model, and price response function was calibrated at segment level. Based on the types of price response function, segments were allocated into one of two groups; the group using the level of price as the quality indicator or the group not using the level of price as that. Then, characteristics of both groups were compared in terms of product attributes and demographic variables.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.77-81
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• 2014

This paper propose a method to detect moving object and locating in real time from video sequence. first the proposed method extract moving object by differencing two consecutive frames from the video sequence. If the interval between captured two frames is long, it cause to generate fake moving object as tail of the real moving object. secondly this paper proposed method to overcome this problem by using doppler effects and HSV color model. finally the object segmentation and locating is done by combining the result that obtained from steps above. The proposed method has 99.2% of detection rate in practical and also this method is comparatively speed than other similar methods those proposed in past. Since the complexity of the algorithm is directly affects to the speed of the system, the proposed method can be used as low complexity algorithm for real time moving object detection.

• Journal of Fashion Business
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• v.19 no.2
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• pp.69-84
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• 2015

The objective of this study is to investigate the intention to adopt smart wear, based on extended UTAUT model. We examined the effects of performance expectancy (PE), effort expectancy (EE), hedonic motivation (HE), social influence (SI), facilitating conditions (FC), and price value (PV) on the intended adoption of smart watch and smart shoes, respectively. In addition, moderating effects of gender, age, and innovation resistance were examined. An online survey was conducted, comprised of 2030 consumers who were aware of smart watch or smart shoes. In total, 393 responses were analyzed. About 50.4% were male, and 44.8% were in their 20's. An exploratory factor analysis generated five factors - PE & HM, EE, SI, FC, and PV- which were employed as independent variables in the multiple regression models. PE & HM, PV, and SI influenced on the intention to use both smart devices. FC showed the significant effect only on the intention to adopt the smart watch. In terms of gender differences, SI and PV were the important predictors of the intention to adopt the smart watch in the female group only. With respect to age difference, SI was very effective in explaining the intention of individuals in their 30's to adopt smart wear. Among the low innovation resistance group, SI was significant predictor, while PE & HE and PV were significant among the high resistance group. The findings provide useful information about the possibility of the adoption of smart wear, and new insight into market segmentation.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.17-25
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• 2019

In this paper, we introduce the technology to convert a single human image into a fashion show animation video clip. The technology can help the customers confirm the dynamic fitting result when combined with the virtual try on technique as well as the interesting experience to a normal person of being a fashion model. We developed an extended technique of full human 2D to 3D inverse modeling based on SMPLify human body inverse modeling technique, and a rigged model animation method. The 3D shape deformation of the full human from the body model was performed by 2 part deformation in the image domain and reconstruction using the estimated depth information. The quality of resultant animation videos are made to be publically available for evaluation. We consider it is a promising approach for commercial application when supplemented with the post - processing technology such as image segmentation technique, mapping technique and restoration technique of obscured area.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.39-47
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• 2016

Geographic information system (GIS) based renewable energy resource map including potential analysis can play a crucial role not only to develop the national plan for renewable energy deployment but also to make strategic investment decision in the private sector. Korea Institute of Energy Research (KIER) has been developing domestic maps about several resources such as solar, wind, hydro, biomass, and geothermal, as well as conducting research on methodologies for potential analysis. Furthermore, the institute is trying to transfer related technologies and know-how to foreign countries, recently. In this context, the main purpose of this study is to introduce the business model of renewable energy resource map. From the value chain analysis, we focus on the government-side market in foreign countries, such as the development of the national level renewable energy resource map and the support of the national renewable energy plan. For about 180 countries, we segment the customers according to the consideration of economic capacity, renewable energy resource capacity, existence of renewable resource map, current portion of renewable energy facility capacity, and renewable energy policies, and we conclude that the target customers are non-Organization for Economic Co-operation and Development (non-OECD) countries or some OECD countries, their per capita GDP are under the average among OECD countries, that do not have renewable resource map yet. We segment the target customers into four groups, and suggest different strategies for market positioning and financing strategy based on Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis. This study can help to develop the business strategy about the development of renewable energy resource map in foreign countries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1800-1817
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• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.