• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.

• Journal of Global Scholars of Marketing Science
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• v.13
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• pp.209-229
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• 2004

Although the site evaluation factors that affect the navigation pattern are well documented, the attempt to explore the difference in the relationship between navigation pattern and site evaluation factors by post hoc segmentation approach has been relatively rare. For this purpose, this study constructs the structure equation model using web-evaluation data and log file of a community site with 300,000 members. And then it applies the structure equation model to each segment. Each segment is identified by mixture model. Mixture model is to unmix the sample, to identify the segments, and to estimate the parameters of the density function underlying the observed data within each segment. The study examines the opportunity to increase GFI, using mixture model which supposes heterogeneous groups in the users, not through specification search by modification index from structure equation model. This study finds out that AGFI increases from 0.819 at total sample to 0.927, 0.930, 0.928, 0.929 for each 4 segments in the case of the community site. The results confirm that segment level approach is more effective than model modification when model is robust in terms of theoretical background. Furthermore, we can identify a heterogeneous navigation pattern and site evaluation variation in the community website at segment level.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.149-157
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• 2011

In this paper, we propose a new cardiac disorder classification method using an support vector machine (SVM) to combine hidden Markov model (HMM) and murmur existence information. Using cepstral features and the HMM Viterbi algorithm, we segment input heart sound signals into HMM states for each cardiac disorder model and compute log-likelihood (score) for every state in the model. To exploit the temporal position characteristics of murmur signals, we divide the input signals into two subbands and compute murmur probability of every subband of each frame, and obtain the murmur score for each state by using the state segmentation information obtained from the Viterbi algorithm. With an input vector containing the HMM state scores and the murmur scores for all cardiac disorder models, SVM finally decides the cardiac disorder category. In cardiac disorder classification experimental results, the proposed method shows the relatively improvement rate of 20.4 % compared to the HMM-based classifier with the conventional cepstral features.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1133-1144
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• 2017

Cluster analysis with meteorological data allows to segment meteorological region based on meteorological characteristics. By the way, meteorological observed data are not adequate for cluster analysis because meteorological stations which observe the data are located not uniformly. Therefore the clustering of meteorological observed data cannot reflect the climate characteristic of South Korea properly. The clustering of $5km{\times}5km$ gridded data derived from a numerical model, on the other hand, reflect it evenly. In this study, we analyzed long-term grid data for temperatures and precipitation using cluster analysis. Due to the monthly difference of climate characteristics, clustering was performed by month. As the result of K-Means cluster analysis is so sensitive to initial values, we used initial values with Ward method which is hierarchical cluster analysis method. Based on clustering of gridded data, cluster of meteorological stations were determined. As a result, clustering of meteorological stations in South Korea has been made spatio-temporal segmentation.

• Transactions of the KSME C: Technology and Education
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• v.2 no.2
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• pp.73-80
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• 2014

Drug delivery in human upper airway was studied by the numerical simulation of oral airflow. We created an anatomically accurate upper airway model from CT scan data by using a medical image processing software (Mimics). The upper airway was composed of oral cavity, pharynx, larynx, trachea, and second generations of branches. Thin sliced CT data and meticulous refinement of model surface under the ENT doctor's advice provided more sophisticated nasal cavity models. With this 3D upper airway models, numerical simulation was conducted by ANSYS/FLUENT. The steady inspiratory airflows in that model was solved numerically for the case of flow rate of 250 mL/s with drug-laden spray(Q= 20, 40, 60 mL/s). Optimal parameters for mechanical drug aerosol targeting of predetermined areas was to be computed, for a given representative upper airways. From numerical flow visualization results, as flow-rate of drug-laden spray increases, the drag spray residue in oral cavity was increased and the distribution of drug spray in trachea and branches became more homogeneous.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.1-10
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• 2012

Recent spatial information technology has brought innovative improvement in both efficiency and accuracy. Especially, airborne LiDAR system(ALS) is one of the practical sensors to obtain 3D spatial information. Constructing reliable 3D spatial data infrastructure is world wide issue and most of the significant tasks involved with modeling manmade objects. This study aims to create a test data set for developing automatic building modeling methods by simulating point cloud data. The data simulates various roof types including gable, pyramid, dome, and combined polyhedron shapes. In this study, a robust bottom-up method to segment surface patches was proposed for generating building models automatically by determining model key points of the objects. The results show that building roofs composed of the segmented patches could be modeled by appropriate mathematical functions and the model key points. Thus, 3D digitizing man made objects could be automated for digital mapping purpose.

• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.7
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• pp.1000-1014
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• 2013

This study identifies the determinants that influence attitude toward and the intention to use mobile shopping services through fashion applications (apps) based on the technology acceptance model. In addition, gender and age group differences were examined. Data were collected from subjects who have used smartphone fashion related apps; subsequently, a total of 327 data were analyzed. About 46% of respondents were males, with a mean age of 34.4 years that ranged from 20 to 49 years old. Multiple regression models were developed based on the research model. Perceived usefulness, perceived ease of use, perceived enjoyment, perceived risks (security risk and quality risk), fashion involvement, and fashion app attributes (product attributes and service attributes) were employed as predictors of attitudes towards mobile shopping. Attitudes towards mobile shopping and subjective norms with the aforementioned variables measured the intention to use. Attitudes towards mobile shopping were predicted by perceived enjoyment, perceived usefulness, and service attributes. Attitudes toward mobile shopping and subjective norms were the most important predictors of the intention to use. Gender differences were found in that service attributes were significant for attitudes towards mobile shopping only in the male model. Age differences were also found and perceived usefulness was the most important predictor of attitudes toward mobile shopping among those in their 20's; however, perceived enjoyment was the most important among those in their 30's and 40's. Quality risk was only significant to explain intention to use among those in their 40's. The findings of this study are useful to understand the possibility of the adoption of mobile shopping though fashion apps and provide basic insight into market segmentation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.22-29
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• 2010

A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.12
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• pp.1980-1991
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• 2010

This study proposes the O P E N Triad framework as a future set of tools and perspectives for individual members and institutes to further their professional and academic potential as well as prospect and vitalize the future of the Korean Clothing and Textiles discipline through a global perspective. The millennial generation desires On-demand, Personal, Engaging, and Networked (O P E N) experiences effecting cultural change for creative and influential interaction in transactions, communication, and education. O P E N Individuals offers a WebSphere model as a holistic learning system that has a synergizing value of education across academic courses, industries, and cultures. Through a digitalized and virtualized class, it complements relevant technologies already familiar to the student population. By employing environmental scanning approaches, the most influential and viable future global issues related to the clothing and textiles discipline are identified and dialogued within O P E N Institutes. For future clothing and textiles institutes, this scanning allows them to be open to new ideas, to focus on inter-engagements, to collaborate among individuals, to associate as a part of web of people, organizations, and ideas, to personalize an institutes curricula, and to dialogue generative knowledge. O P E N Industries reveals three dominant future issues that cross academia and industry, sustainability, supply chain management, and social networking. In-depth interviews with U.S. industry experts identified interdependent gaps in global consumer experience practices and suggested the following gaps as future research areas: a standardized business model to the entrepreneurial model, strategic management to a sustainable competitive advantage, standardized to differentiated products, services and operations, market segmentation to global consumer clusters, business-driven marketplaces to consumer-engaged marketspaces, and excellent services to optimal experience. This O P E N Triad framework empowers millennial students, universities, and industries to anticipate and prepare for a radically changing world.