• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2839-2852
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• 2013

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

• Fashion & Textile Research Journal
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• v.8 no.4
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• pp.427-432
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• 2006

The purpose of this study was 1) to segment the market of inbound Japanese tourists based on the importance of tour activity that tourists perceived and 2) to examine the behavior of each segmentation purchasing cultural fashion items in Korea. Data were collected using a self-administered questionnaire survey in Seoul. Clustering analysis, Chisquare, and ANOVA test were used to conduct the data analysis on 288 out of 400 questionnaires. The inbound Japanese tourists market was segmented into 3 groups; culture oriented group, shopping oriented group, and multi-activity group. Three groups were significantly different in terms of age, income, purchase amount, purchase criteria, and degree of shopping satisfaction. Marketing strategies for segmented markets were discussed.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2019.05a
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• pp.275-276
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• 2019

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.411-426
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• 2023

Purpose - The purpose of this study was to explore and diagnose the characteristics and behavioural patterns of rural tourists after COVID-19 using decision tree analysis to classify and identify key segmentation groups. Design/methodology/approach - The CHAID algorithm was used as the analysis technique for the decision tree. The explanatory variables used in the analysis of each decision tree model were demographic variables and rural tourism usage behaviour and perception variables, and the target variables were the preferences of rural tourists' activities after COVID-19. From the Rural Tourism 2020 survey data, 614 samples with rural tourism experience were extracted and used in the analysis. Findings - The variables that significantly explained the preference for each type of rural tourism activity after COVID-19 were rural tourism safety perception, repeated visits to the region, rural tourism priority activity, rural tourism accommodation experience, gender, age group, marital status, occupation, and education level. Among them, rural tourism safety perception was the most important explanatory variable in each analysis model. Research implications or Originality - Overall, to promote rural tourism, it is necessary to enhance the safety image of rural tourism, strengthen loyalty programs for repeat visitors, and develop customized products that reflect the preferred trends of rural tourism.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.779-786
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• 2012

The purpose of this study was to segment latent horse riding market. Demographic characteristic, psychological characteristic, lifestyle, and perception of horse riding factor were included in the segmentation basis but, only lifestyle was useful factor for horse riding industry. The statistical techniques for data analysis were descriptive analysis, explanatory factor analysis, confirmatory factor analysis, reliability analysis, cluster analysis, crosstab analysis, and Chi-square analysis. The result showed that sport activity, activity, and passivity factor were identified by lifestyle. The latent market of horse riding had two segment markets. First segment was known as a sport activity oriented group and the other is nonactivity oriented group. According to three demographic variables and preference of horse riding were statistical significant at the level of .05.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.17-34
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• 2007

Organizations have realized the importance of CRM. To obtain the maximum possible lifetime value from a customer base, it is critical that customer data is analyzed to understand patterns of customer response. As customer databases assume gigantic proportions due to Internet and e-commerce activity, data-mining-based market segmentation becomes crucial for understanding customers. Here we raise a question and some issues of using single SOM approach for clustering while proposing multiple self-organizing maps approach. This methodology exploits additional themes on the attributes that characterize customers in a typical CRM system. Since this additional theme is usually ignored by traditional market segmentation techniques we here suggest careful application of SOM for market segmentation.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.328-332
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• 2015

By logging position data from GPS-equipped construction machines, we re-create daily activities as 3D animations to analyze performance and facilitate look-ahead scheduling. The 3D animation enables going back to any point in time and space to observe the activities as they took place. By segmenting data into a set of activities, it is possible to obtain actual measures of performance such as cycle times, production, speed profiles and idle times. The measures of performance can then be compared to those expected (e.g., theoretical speed profiles vs. observed profiles), and instances of significant difference can be flagged for further investigation. Idle times and queues that exceed prescribed thresholds can also be identified. In general, many of the traditional real-time performance analyses can be performed after the fact. Situations of interest can be identified automatically and the events in this manner enhances effective performance improvement in construction. The proposed research is explained and demonstrated using a real push-loaded earthmoving operation.

• Advances in robotics research
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• v.2 no.2
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

• Journal of Environmental Science International
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• v.27 no.10
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• pp.865-874
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• 2018

Fruit trees are a key part of agriculture in rural areas and have recently been a part of ecotourism or agrotourism. This study analyzes user segmentation based on user motivation to determine characteristics of potential customers in fruit tree farms, and thereby develop fruit tree experience and educational programs. We conducted a survey of 253 potential customers of fruit tree experience programs in September 2017. Data were evaluated using factor and cluster analyses. The results of the cluster analysis identified four distinct segments based on potential customers' motivations, that is, activity-oriented, learning-oriented, leisure-oriented, and purchase-oriented. These clusters showed that significant differences in the preference of potential customers exist. Different markets were segmented based on the benefits sought by users. The segments' characteristics were identified and activities relevant to each segment were proposed for rural tourism. Lastly, this study suggests directions for development of fruit tree farm experience and educational programs.