• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.35-40
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• 2021

In this paper, we proposed a multi-subject-level sentiment analysis model for user reviews using the Latent Dirichlet Allocation (LDA) method targeting user-generated content (UGC). Data were collected from users' online reviews of hotels in major tourist cities in the world, and 30 hotel-related topics were extracted using the entire user reviews through the LDA technique. Six major hotel-related themes (Cleanliness, Location, Rooms, Service, Sleep Quality, and Value) were selected from the extracted themes, and emotions were evaluated for sentences corresponding to six themes in each user review in the proposed sentiment analysis model. Sentiment was analyzed using a dictionary. In addition, the performance of the proposed sentiment analysis model was evaluated by comparing the emotional values for each subject in the user reviews and the detailed scores evaluated by the user directly for each hotel attribute. As a result of analyzing the values of accuracy and recall of the proposed sentiment analysis model, it was analyzed that the efficiency was high.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.441-448
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• 2014

USN(Ubiquitous Sensor Network) application software has a characteristic that it controls a variety of sensor nodes based on the various target operating systems. Accordingly, many researches for efficient development of USN application software are being performed. In this paper, the attributes design technique to support attribute-based development of USN node software for multi-platform is proposed. In the proposed technique, the method to design attributes for modeling Platform Independent Model and Platform Specific Model is presented. When using the proposed technique, productivity of software development will be increased because node software design for multi-platform is easily performed by selecting values of attributes. Also, maintainability of software will be increased because node software is easily regenerated by changing attributes according to the changes of operating systems.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.842-854
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• 2001

Today\`s first generation shopping agent is limited to comparing merchant offerings usually on price instead of their full range of attributes. Even in the full range comparison, there is not a good model which considers the overall features in the negotiation process. Therefore, the negotiation model needs to be extended to include negotiations over the more attributes. In this paper, we propose a negotiation model in the agent-mediated electronic commerce to negotiate over prices, product features, warranties and service policies based on utility theory and simple heuristics. We will describe a prototype agent-mediated electronic commerce framework called Pmart. This framework provides the software reuse and the extensibility based on the object-oriented technology. It is implemented on Windows-based platforms using Java and CORBA for the network transparency and platform independence.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.73-78
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• 2002

In this paper, we propose a constrained optimization model for conjoint analysis (a preference decomposition technique) to improve parameter estimation by restricting the relative importance of the attributes to an extent as decided by the respondents. Quite simply, respondents are asked to provide some pairwise attribute comparisons that are then incorporated as additional constraints in a linear programming model that estimates the partial preference values. This data collection method is typical in the analytic hierarchy process. Results of a simulation study show the new model can improve the predictive accuracy in partial value estimation by ordinal east squares (OLS) regression.

• Journal of the Korean housing association
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• v.25 no.4
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• pp.59-72
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• 2014

This study proposes a data-mining analysis method for examining variable multi-attribute big-data, which is considered to be more applicable in social science using a Correspondence Analysis of variables obtained by AIC model selection. The proposed method was applied on the Seoul Survey from 2005 to 2010 in order to extract interesting rules or patterns on characteristics of single household. The results found as follows. Firstly, this paper illustrated that the proposed method is efficiently able to apply on a big dataset of huge categorical multi attributes variables. Secondly, as a result of Seoul Survey analysis, it has been found that the more dissatisfied with residential environment the higher tendency of residential mobility in single household. Thirdly, it turned out that there are three types of single households based on the characteristics of their demographic characteristics, and it was different from recognition of home and partner of counselling by the three types of single households. Fourthly, this paper extracted eight significant variables with a spatial aggregated dataset which are highly correlated to the ratio of occupancy of single household in 25 Seoul Municipals, and to conclude, it investigated the relation between spatial distribution of single households and their demographic statistics based on the six divided groups obtained by Cluster Analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.2
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• pp.143-155
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• 2000

With the increasing needs for the integrated use of topographic and cadastral data in order to build an efficient geo-spatial information system. it is urgently necessary to research into its solution. The intention of this study is to detect error types of data and to propose adjustment methods for solving the problems caused by integrating topographic and cadastral data. For this purpose a primary integrated data model is created to link attribute data(land management system) and graphic data within cadastral information in the first step. In next, a secondary integrated data model based on the improved method is formed to coincide the graphic data of cadastral map with that of topographic map. At the first, because a numerous error types md sources caused by separate management of graphic and attribute data are easily checked, it is possible to suggest an improved method to correct these errors using the primary integrated data model. In addition, the accuracy in position and area with coordinate transformation method based on multi-block adjustment is more efficient than rubber-sheeting method. As a result, the secondary integrated data model could be built by harmonizing cadastral map with topographic map using the improved solution.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.51-59
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• 2024

Collecting a dataset with a corresponding labeled gaze vector requires a high cost in the gaze estimation field. In this paper, we suggest a data augmentation of manipulating the gaze of an original image, which improves the accuracy of the gaze estimation model when the number of given gaze labels is restricted. By conducting multi-class gaze bin classification as an auxiliary task and adjusting the latent variable of the diffusion model, the model semantically edits the gaze from the original image. We manipulate a non-binary attribute, pitch and yaw of gaze vector to a desired range and uses the edited image as an augmented train data. The improved gaze accuracy of the gaze estimation network in the semi-supervised learning validates the effectiveness of our data augmentation, especially when the number of gaze labels is 50k or less.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.482-489
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• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.635-645
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• 2007

Component technology has been widely accepted as an effective way for building software systems with reusable components, and Microsoft (MS) .NET is one of the recent representative component technologies. Model Driven Architecture (MDA) is a new development paradigm which generates software by transforming design models automatically and incrementally. Transformation of structural models in MDA has been successfully applied. However, transformation of dynamic models and pervasive services, such as transaction service, security service, synchronization service and object pooling are largely remains as an area for further research. The recent enterprise system has multi tier distributed architecture, and the functionality of early mentioned pervasive services is essential for this architecture. .NET platform can implement Component Object Model+ (COM+) component for supporting pervasive services by specify Attribute code. In this paper, we specify the functionalities of the COM+ component offering pervasive services, and then those functionalities are defined by UML profile. By using the profile, the Platform Specific Model (PSM) for .NET/C# is specified, and .NET components are automatically generated through our tool. The development productivity, extensibility, portability, and maintenance of software can be dramatically improved by using of the proposed methods.