• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.14-26
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• 2018

The Biotope Area Ratio (BAR) is a quantitative pre-planning index for sustainable development and an integrated indicator for the balanced development of buildings and outdoor spaces. However, it has been pointed out that there are problems in operations management: errors in area calculation, insufficiency in the underground soil condition and depth, reduction in biotope area after construction, and functional failure as a pre-planning index. To address these problems, this study proposes implementing LIM. Since the weights of the BAR are mainly decided by the underground soil condition and depth with land cover types, the study focused on the terrain and pavements. The model should conform to BIM guidelines and standards provided by government agencies and professional organizations. Thus, the scope and Level Of Detail (LOD) of the model were defined, and the method to build a model with BIM software was developed. An apartment complex on sloping ground was selected as a case study, a 3D terrain modeled, paving libraries created with property information on the BAR, and a LIM model completed for the site. Then the BAR was calculated and construction documents were created with the BAR table and pavement details. As results of the study, it was found that the application of the criteria on the BAR and calculation became accurate, and the efficiency of design tasks was improved by LIM. It also enabled the performance of evidence-based design on the terrain and underground structures. To adopt LIM, it is necessary to create and distribute LIM library manuals or templates, and build library content that comply with KBIMS standards. The government policy must also have practitioners submit BIM models in the certification system. Since it is expected that the criteria on planting types in the BAR will be expanded, further research is needed to build and utilize the information model for planting materials.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.4
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• pp.11-24
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• 2010

The main focus of this research is the establishment of a systemic evaluation model based on objective evaluation indices, which are drawn to assess the experiencing of nature and recreational value at the level of the district unit. First of all, as a result of a literature review, a total of 10 indices can be drawn including vegetation structure, pavement rate, and hemeroby to evaluate an assessment of natural experiences and recreational value. Also, as a result of expert survey analysis, all evaluation index items were above 4.4, which is a high importance average. Hemeroby and unique landscape factor items in particular were above 5.8, which is very high. In addition, as a result of implementing a factor analysis to classify evaluation indices according to characteristics, three factors arise: 'landscape structure and quality of natural experience', 'typical availability', and 'quality of aesthetic and visual sense.' Based on the above survey analysis results, the 'quality of aesthetic and visual sense' was the highest, at 3.510. The classification 'landscape structure and quality of natural experience' was the lowest, at 3.035. A systemic value evaluation model was established by comprehensively analyzing these results. To verify the validity of the evaluation model drawn, real sites are selected and applied. First of all, as a result of a biotope types classification of sites, biotope type groups are classified into a total of 13 including the stream biotope while its subordinate biotope types are classified into a total of 61 groups. Lastly, as a result of biotope value evaluation, which was a previously established evaluation model, there are a total of 16 types including vegetation-abundant natural rivers and small-scale woodlands near forests in grade I. There are 9 types in grade II, 8 in grade III, 8 in grade IV, 19 in the least-valuable grade V.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.6
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• pp.43-54
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• 2009

This study attempted to develop a model for selecting sites for ecologically effective, multi-functional wetlands during the environmental and ecological planning stage, prior to land use Planning. This model was developed with an emphasis upon the creation of a water circulation system for a newly-created city, dispersing and retaining the run-off that is increased due to urbanization and securing spaces to create wetlands that can promote urban biodiversity. A series of Precesses for selecting sites for wetland restoration and creation - watershed analysis, selection of evaluation items, calculation of weights, reparation of thematic maps and synthesis - were incorporated into the model. Its potentials and limitations were examined by applying it to the recently-planned WiRae New Community Development Area, which is located in the Seoul metropolitan region. At the watershed analysis stage, the site was divided into 13 sub-catchment areas. Inflow to watersheds including the area was $3,020,765m^3$ Run-off before and after development is estimated as $1,901,969m^3$ and $1,970,735{\sim}2,039,502m^3$, respectively. The total storage capacity required in the development area amounts to $68,766{\sim}137,533m^3$. When thematic maps were overlapped during the selection stage for wetland sites, 13 sub-catchment areas were prioritized for wetland restoration and creation. The locations and areas for retaining run-off showed that various types of wetlands, including retaining wetlands (area wetlands), riverine wetlands (linear wetlands) and pond wetlands (point wetlands), can be created and that they can be systematically connected. By providing a basic framework for the water circulation system plan of an entire city, it may be used effectively in the space planning stage, such as planning an urban eco-network through integration with greet areas. In order to estimate reasonable run-off and create an adequate water circulation system however, a feedback process following land use planning is required. This study strived to promote urban changes in a positive direction while minimizing urban changes in negative forms.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1143-1152
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• 2012

We established a top-down methodology to estimate carbon footprint as national mean value (reference) with the statistical data on agri-livestock incomes in 2007. We also established LCI (life cycle inventory) DB by a bottom-up methodology with the data obtained from interview with farmers from 4 large-scale farms at Gunsan, Jeollabuk-do province to estimate carbon footprint in 2011. This study was carried out to compare top-down methodology and bottom-up methodology in performing LCA (life cycle assessment) to analyze the difference in GHGs (greenhouse gases) emission and carbon footprint under conventional rice cultivation system. Results of LCI analysis showed that most of $CO_2$ was emitted during fertilizer production and rice cultivation, whereas $CH_4$ and $N_2O$ were mostly emitted during rice cultivation. The carbon footprints on conventional rice production system were 2.39E+00 kg $CO_2$-eq. $kg^{-1}$ by top-down methodology, whereas 1.04E+00 kg $CO_2$-eq. $kg^{-1}$ by bottom-up methodology. The amount of agro-materials input during the entire rice cultivation for the two methodologies was similar. The amount of agro-materials input for the bottom-up methodology was sometimes greater than that for top-down methodology. While carbon footprint by the bottom-up methodology was smaller than that by the top-down methodology due to higher yield per cropping season by the bottom-up methodology. Under the conventional rice production system, fertilizer production showed the highest contribution to the environmental impacts on most categories except GWP (global warming potential) category. Rice cultivation was the highest contribution to the environmental impacts on GWP category under the conventional rice production system. The main factors of carbon footprints under the conventional rice production system were $CH_4$ emission from rice paddy field, the amount of fertilizer input and rice yield. Results of this study will be used for establishing baseline data for estimating carbon footprint from 'low carbon certification pilot project' as well as for developing farming methods of reducing $CO_2$ emission from rice paddy fields.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.98-108
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• 2013

The number of Hi-Pass lanes became 793 lanes at 316 expressway tollgates in 2011 due to the increase in the Hi-Pass use. In spite of the increase in the number of Hi-Pass lanes, there have been increased potential risks in tollgates where vehicles using a Hi-Pass lane must weave with other vehicles using a TCS lane. Therefore, there is a need for study on the safety in tollgates. To this end, this study aims at developing a methodology to evaluate the performance measures of diverse location countermeasures of Hi-Pass lanes in an efficient and systematic way. This study measured the mobility, safety and the convenience of installation and operation of Hi-Pass lanes using a microscopic traffic simulation tool, the surrogate safety assessment model and survey. In addition, this study aggregated the above three performance indexes using weight factors estimated using the AHP technique. For the test site, Dongsuwon interchange was selected. After building the microscopic traffic simulation model for the test site, the location countermeasures of Hi-Pass lanes applicable to the test site were compared with each other in terms of the mobility, safety and installing and operating convenience. As a result, there has been no apparent difference in mobility index based on delays. However, the countermeasures where Hi-Pass lanes are located in inside lanes generally showed better safety performance based on the number of conflicts. In addition, countermeasures with neighboring Hi-Pass lanes were favorable in terms of the safety and the convenience of installation and operation. The methodology proposed in this study was found to be useful to support decision makings by providing critical and quantitative information regarding the mobility, safety and the convenience of installation and operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1173-1183
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• 2006

In this paper, we propose an efficient method for improving visual quality of AR-FGS (Adaptive Reference FGS) which is adopted as a key scheme for SVC (Scalable Video Coding) or H.264 scalable extension. The standard FGS (Fine Granularity Scalability) adopts AR-FGS that introduces temporal prediction into FGS layer by using a high quality reference signal which is constructed by the weighted average between the base layer reconstructed imageand enhancement reference to improve the coding efficiency in the FGS layer. However, when the enhancement stream is truncated at certain bitstream position in transmission, the rest of the data of the FGS layer will not be available at the FGS decoder. Thus the most noticeable problem of using the enhancement layer in prediction is the degraded visual quality caused by drifting because of the mismatch between the reference frame used by the FGS encoder and that by the decoder. To solve this problem, we exploit the principle of cyclical block coding that is used to encode quantized transform coefficients in a cyclical manner in the FGS layer. Encoding block coefficients in a cyclical manner places 'higher-value' bits earlier in the bitstream. The quantized transform coefficients included in the ealry coding cycle of cyclical block coding have higher probability to be correctly received and decoded than the others included in the later cycle of the cyclical block coding. Therefore, we can minimize visual quality degradation caused by bitstream truncation by adjusting weighting factor to control the contribution of the bitstream produced in each coding cycle of cyclical block coding when constructing the enhancement layer reference frame. It is shown by simulations that the improved AR-FGS scheme outperforms the standard AR-FGS by about 1 dB in maximum in the reconstructed visual quality.