• Journal of the Korean Institute of Landscape Architecture
• /
• v.52 no.2
• /
• pp.51-63
• /
• 2024

While the government policy to fully adopt BIM in the construction sector is being implemented, the construction and utilization of landscape BIM models are facing challenges due to problems such as limitations in BIM authoring tools, difficulties in modeling natural materials, and a shortage in BIM content including libraries. In particular, plants, fundamental design elements in the field of landscape architecture, must be included in BIM models, yet they are often omitted during the modeling process, or necessary information is not included, which further compromises the quality of the BIM data. This study aimed to contribute to the construction and utilization of landscape BIM models by developing a plant library that complies with BIM standards and is applicable to the landscape industry. The plant library of trees and shrubs was developed in Revit by modeling 3D shapes and collecting attribute items. The geometric information is simplified to express the unique characteristics of each plant species at LOD200, LOD300, and LOD350 levels. The attribute information includes properties on plant species identification, such as species name, specifications, and quantity estimation, as well as ecological attributes and environmental performance information, totaling 24 items. The names of the files were given so that the hierarchy of an object in the landscape field could be revealed and the object name could classify the plant itself. Its usability was examined by building a landscape BIM model of an apartment complex. The result showed that the plant library facilitated the construction process of the landscape BIM model. It was also confirmed that the library was properly operated in the basic utilization of the BIM model, such as 2D documentation, quantity takeoff, and design review. However, the library lacked ground cover, and had limitations in those variables such as the environmental performance of plants because various databases for some materials have not yet been established. Further efforts are needed to develop BIM modeling tools, techniques, and various databases for natural materials. Moreover, entities and systems responsible for creating, managing, distributing, and disseminating BIM libraries must be established.

• Journal of Global Scholars of Marketing Science
• /
• v.19 no.4
• /
• pp.1-10
• /
• 2009

The purpose of this study was to categorize the gap between coffee shop 'importance' (as perceived by customers before patronizing the coffee shop) and 'satisfaction' (perception of customers after patronizing the coffee shop) as positive or negative and to analyze the effect of these gaps on purchasing behavior. To do this, I used the gap between importance and satisfaction regarding the choice of a coffee shop as the explanatory variable and performed an empirical analysis of the direction and size of the effect of the gap on purchasing behavior (overall satisfaction, willingness-to-revisit) by applying the Ordered Probit Model (OPM). A previous study that used IPA to evaluate the effects of gaps estimated the direction and size of a quadrant but failed to analyze the effect of gaps on customers. In this study, I evaluated the effects of positive and negative gaps on customer satisfaction and willingness-to-revisit. Using OPM, I quantified the effect of positive and negative gaps on overall customer satisfaction and willingness-to-revisit. Per-head expenditure, frequency of visits, and coffee-purchasing place had the most positive effects on overall customer satisfaction. Frequency of visits, followed by per-head expenditure and then coffee-purchasing place, had the most positive impact on willingness-to-visit. Thus per-head expenditure and frequency of visits had the greatest positive effects on overall satisfaction and willingness-to-revisit. This finding implies that the higher the actual satisfaction (gap) of customers who spend KRW5,000 or more once or more per week at coffee shops is, the higher their overall satisfaction and willingness-to-revisit are. Despite the fact that economical efficiency had a significant effect on overall satisfaction and willingness-to-revisit, college and university students still use coffee shops and are willing to spend KRW5,000 because they do not only purchase coffee as a product itself, but use the coffee shop for other activities, such as working, meeting friends, or relaxing. College and university students also access the Internet in coffee shops via personal laptops, watch movies, and study; thus, coffee shops should provide their customers with the appropriate facilities and services. The fact that a positive gap for coffee shop brand had a positive effect on willingness-to-revisit implies that the higher the level of customer satisfaction, the greater the willingness-to-revisit. A negative gap for this factor, on the other hand, implies that the lower the level of customer satisfaction, the lower the willingness-to-revisit. Thus, the brand factor has a comparatively greater effect on satisfaction than the other factors evaluated in this study. Given that the domestic coffee culture is becoming more upscale and college/university students are sensitive to this trend, students are attentive to brands. In most upscale coffee shops in Korea, the outer wall is built out of glass that can be opened, the interiors are exotic with an open kitchen. These upscale coffee shops function as landmarks and match the taste of college/university students. Coffee shops in Korea have become a cultural brand. To make customers feel that coffee shops are upscale, good quality establishments and measures to provide better services in terms of brand factor should be instituted. The intensified competition among coffee shop brands in Korea as a result of the booming industry indicates that provision of additional services is needed to differentiate competitors. These customers can also use a scanner free of charge. Another strategy that can be used to boost brands could be to provide and operate a seminar room for seminars and group study. If coffee shops adopt these types of strategies, college/university students would be more likely to consider the expenses they incur worthwhile and, subsequently, they would be more likely to be satisfied with the brands of these coffee shops, with an associated increase in their willingness-to-revisit. Gender and study year had the most negative effects on overall satisfaction and willingness-to-revisit. Female students were more likely to be satisfied and be willing to return than male students, and third and fourth-year students were more likely to be satisfied and willing-to-return than first or second-year students. Students who drink coffee, read books, and use laptops alone at coffee shops are easily noticeable. High-grade students tend to visit coffee shops alone in order to use their time efficiently for self-development and to find jobs. The economical efficiency factor had the greatest effect on overall satisfaction and willingness-to-revisit in terms of a positive gap. The higher the actual satisfaction (gap) of students with the price of the coffee, the greater their overall satisfaction and willingness-to-revisit. Economical efficiency with a negative gap had a negative effect on willingness-to-revisit, which implies that a less negative gap will result in a greater willingness-to-revisit. Amid worsening market conditions, coffee shops located around colleges/universities are using strategies, such as a point or membership card, strategic alliances with credit-card companies, development of a set menu or seasonal menu, and free coffee-shot services to increase their competitive edge. Product power also had a negative effect in terms of a negative gap, which indicates that a higher negative gap will result in a lower willingness-to-revisit. Because there are many more customers that enjoy coffee in this decade, as compared to previous decades, the new generation of customers, namely college/university students, want various menu items in addition to coffee, and coffee shops should, therefore, add side menu items, such as waffles, rice cakes, cakes, sandwiches, and salads. For example, Starbucks Korea is making efforts to enhance product power by selling rice cakes flavored in strawberry, wormwood, and pumpkin, and providing coffee or cream free of charge. In summary, coffee shops should focus on increasing their economical efficiency, brand, and product power to enhance the satisfaction of college/university students. Because shops adjacent to colleges or universities enjoy a locational advantage, providing differentiated services in terms of economical efficiency, brand, and product power, is likely to increase customer satisfaction and return visits. Coffee shop brands should, therefore, be innovative and embrace change to meet their customers' desires. Because this study only targeted college/university students in Seoul, comparative studies targeting diverse regions and age groups are required to generalize the findings and recommendations of this study.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.47-73
• /
• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.141-154
• /
• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• (The)Study of the Eastern Classic
• /
• no.56
• /
• pp.147-170
• /
• 2014

To divide by the concept of Cheon (天) before and after the period of Song Dynasty: before Song Dynasty; according to the ancient Book of Odes (Sigyeong-詩經), "Cheon (天) gives birth to a large number of people", and, Confucius(孔子) say "Cheon(天) gave me Virtue(德)." Mencius(孟子) say "The person done with all his heart knows Seong(性, personality), so if he knows such Seong(性, personality), then he knows Cheon(天)." In Doctrine of the Mean(中庸), it says "Cheon(天) ordered it to be called - Seong(性, personality)." So, Cheon(天) had a religious meaning, such as Sangje(上帝) - Supreme Ruler. During the Song period, Cheon(天), the source of its existence, had construed as Mugeuk i Taegeuk Non(無極而太極論 - Theory of Supreme Ultimate while being Indeterminate) and Theory of li and ki (iginon-理氣論). Juja (朱子, a honorary name of Juhui, 朱熹) had said a reasonable Cheon(天), that is, Heavenly Principle (天理 - Cheolli) by interpreting Cheon(天) as Taegeuk(太極 - Supreme Polarity) and li(理) of Muwi(無爲 - uncontrived action). That's why Juja had lost the religiosity because of his reasonable frame. The purpose of this dissertation is to identify of the quality of being religious of li(理) on the basis of attribute of Cheon(天) argued by Toegye and Juja. In the text of Seomyeong(西銘 - Western Inscription), we can see their interpretation of the content that Toegye as "西銘考證講義"(Lecture on Historical Research of Western Inscription), and Juja as "西銘解"(Commentary on the Western Inscription). Seomyeong(西銘 - Western Inscription) was expounded as a logic of 'iil bunsu' (理一分殊 - coherence is one and distinguished into many). '理一分殊' means to live in as meaningful as possible according to the human nature that has been bestowed upon thyself. Juja and Toegye both said that in the aspect of 'iil'(理一 - coherence is one), Reverence(事天) ought to be done, but to look into the aspect of 'bunsu'(分殊-distinguished into many), Juja argued that people should follow the order of Heavenly Principle(天理 - Cheolli), and Toegye argued that people should have to perform the filial piety(孝). There are differences in methods of Toegye and Juja on account of distinction between attributes of Cheon(天). Such a distinction affects the attribute of li(理). Juja said divisively that Soiyeon(所以然-why its principle is so) is li(理), and Sodangyeon(所當然-what should be so) is Sa(事-divine project). Toegye argued that Sodangyeon(所當然-what should be so) is indeed li(理). It is the position of Toegye that to know Seong(性-the personality) of Sodangyeon(所當然-what should be so) is the first, rather than to know Cheon(天) of Soiyeon(所以然-why its principle is so) that is out of reach in a faraway place. Seong(性-the personality) is li(理) that bestowed by Cheon(天). In view of discussion about the essence and existence, for Toegye, the existence is the first, rather than the essence. The issues of existence is now enabled to talk about amid the discussion of metaphysics, namely li(理). Different from Juja, a theory noticed in Toegye is the theory of 'Lijado'(理自到). 'Lijado'(理自到) denotes 'Li(理) leads on their own.' It tells that separate from thing-in-itself, there is an energy that moves and oversees the thing. This is an issue of response between "I" as the principal agent and other people. If "I" as the principal agent is sincere to others, the others will come to me insomuch as they will be revealed through me. Here, a problem between the host and guest arises. Toegye perceived this problem that do not see me and others as same, and also do not see me and others as two. This is the logic of 'ilii iiil'(一而二 二而一 - looks like one but two, looks like two but one) of '理一分殊' (coherence is one and distinguished into many). The first thing to do between these two processes is to recognize the existence of 'iil'(理一). Toegye strongly displays a religious attitude identifying Cheon(天)=Li (理)=Sangje(上帝- Supreme Ruler) in the same light.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.55-78
• /
• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.