• 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.26 no.1
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• pp.47-73
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• 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
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.6
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• pp.73-85
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• 2015

This study investigated the extent and magnitude of the woodland edge effects on users' thermal environments according to distance from woodland border. A series of experiments to measure air temperature, relative humidity, wind velocity, MRT and UTCI were conducted over six days between July 31 and August 5, 2015, which corresponded with extremely hot weather, at the south-facing edge of Hadongsongrim(pure Pinus densiflora stands, tree age: $100{\pm}33yr$, tree height: $12.8{\pm}2.7m$, canopy closure: 75%, N $35^{\circ}03^{\prime}34.7^{{\prime}{\prime}}$, E $127^{\circ}44^{\prime}43.3^{{\prime}{\prime}}$, elevation 7~10m) and east-facing edge of Hamyangsangrim (Quercus serrata-Carpinus tschonoskii community, tree age: 102~125yr/58~123yr, tree height: tree layer $18.6{\pm}2.3m/subtree$ layer $5.9{\pm}3.2m/shrub$ layer $0.5{\pm}0.5m$, herbaceous layer coverage ratio 60%, canopy closure: 96%, N $35^{\circ}31^{\prime}28.1^{{\prime}{\prime}}$, E $127^{\circ}43^{\prime}09.8^{{\prime}{\prime}}$, elevation 170~180m) in rural villages of Hadong and Hamyang, Korea. The minus result value of depth means woodland's outside. The depth of edge influence(DEI) on the maximum air temperature, minimum relative humidity and wind speed at maximum air temperature time during the daytime(10:00~17:00) were detected to be $12.7{\pm}4.9$, $15.8{\pm}9.8$ and $23.8{\pm}26.2m$, respectively, in the mature evergreen conifer woodland of Hadongsongrim. These were detected to be $3.7{\pm}2.2$, $4.9{\pm}4.4$ and $2.6{\pm}7.8m$, respectively, in the deciduous broadleaf woodland of Hamyansangrim. The DEI on the maximum 10 minutes average MRT, UTCI from the three-dimensional environment absorbed by the human-biometeorological reference person during the daytime(10:00~17:00) were detected to be $7.1{\pm}1.7$ and $4.3{\pm}4.6m$, respectively, in the relatively sparse woodland of Hadongsongrim. These were detected to be $5.8{\pm}4.9$ and $3.5{\pm}4.1m$, respectively, in the dense and closed woodland of Hadongsongrim. Edge effects on the thermal environments of air temperature, relative humidity, wind speed, MRT and UTCI in the sparse woodland of Hadongsongrim were less pronounced than those recorded in densed and closed woodland of Hamyansangrim. The gradient variation was less steep for maximum 10 minutes average UTCI with at least $4.3{\pm}4.6m$(Hadongsongrim) and $3.5{\pm}4.1m$(Hamyansangrim) being required to stabilize the UTCI at mature woodlands. Therefore it is suggested that the woodlands buffer widths based on the UTCI values should be 3.5~7.6 m(Hamyansangrim) and 4.3~8.9(Hadongsongrim) m on each side of mature woodlands for users' thermal comfort environments. The woodland edge structure should be multi-layered canopies and closed edge for the buffer effect of woodland edge on woodland users' thermal comfort.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.33 no.4
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• pp.251-262
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• 2007

In this study, the antioxidative effects, inhibitory effects on elastase, and components of Aspalathus linearis extracts were investigated. The free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activities ($FSC_{50}$) of extract/fractions of Aspalathus linearis were in the order: 50 % ethanol extract ($11.50\;{\mu}g/mL$) < deglycosylated flavonoid aglycone fraction ($8.47\;{\mu}g/mL$) < ethylacetate fraction ($4.76\;{\mu}g/mL$). Reactive oxygen species (ROS) scavenging activities ($OSC_{50}$) of some Aspalathus linearis extracts on ROS generated in $Fe^{3+}-EDTA/H_2O_2$ system were investigated using the luminol-dependent chemiluminescence assay. The order of ROS scavenging activities were ethylacetate fraction ($OSC_{50},\;4.58\;{\mu}g/mL$) < deglycosylated flavonoid aglycone fraction ($2.20\;{\mu}g/mL$) < 50 % ethanol extract ($1.09\;{\mu}g/mL$). 50 % Ethanol extract showed the most prominent scavenging activity. The protective effects of extract/fractions of Aspalathus linearis on the rose-bengal sensitized photohemolysis of human erythrocytes were investigated. The Aspalathus linearis extracts suppressed photohemolysis in a concentration dependent manner, particularly 50 % ethanol extract exhibited the most prominent celluar protective effect (${\tau}_{50}$, 272.00 min at $50\;{\mu}g/mL$). Aglycone fractions obtained from the deglycosylation reaction of ethylacetate fraction among the Aspalathus linearis extracts, showed 3 bands in TLC and 3 peaks in HPLC experiments (360 nm). Three components were identified as luteolin (composition ratio, 18.24 %), quercetin (58.79), and kaempferol (22.97). TLC chromatogram of ethylacetate fraction of Aspalathus linearis extract revealed 7 bands and HPLC chromatogram showed 9 peaks, which were identified as isoorientin (composition ratio, 14.71 %), orientin (28.84 %), vitexin (5.63 %), rutin and isovitexin (12.73 %), hyperoside (9.24 %), isoquercitrin (5.40 %), luteolin (1.48 %), quercetin (17.61 %) and kaempferol (4.59 %) in the order of elution time. The inhibitory effect of aglycone fraction on elastase ($IC_{50},\;9.08\;{\mu}g/mL$) was very high. These results indicate that extract/fractions of Aspalathus linearis can function as antioxidants in biological systems, particularly skin exposed to UV radiation by scavenging $^1O_2$ and other ROS, and protect cellular membranes against ROS. And component analysis of Aspalathus linearis extract and inhibitory activity on elastase of the aglycone fraction could be applicable to new functional cosmetics for smoothing wrinkles.

• Radiation Oncology Journal
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• v.21 no.1
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• pp.54-65
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• 2003

Purpose : To analyze the gene expression Profiles of uterine ceulcal cancer, and its variation after radiation therapy, with or without concurrent chemotherapy, using a CDNA microarray. Materials and Methods :Sixteen patients, 8 with squamous ceil carcinomas of the uterine cervix, who were treated with radiation alone, and the other 8 treated w14h concurrent chemo-radiation, were Included in the study. Before the starling of the treatment, tumor biopsies were carried out, and the second time biopsies were peformed after a radiation dose of 16.2$\~$27 Gy. Three normal cervix tissues were used as a control group. The microarray experiments were peformed with 5 groups of the total RNAs extracted individually and then admixed as control, pre-radiation therapy alone, during-radiation therapy alone, pre-chemoradiation therapy, and during-chemoradlation therapy. The 33P-iabeled CDNAS were synthesized from the total RNAs of each group, by reverse transcription, and then they were hybridized to the CDNA microarray membrane. The gene expression of each microarrays was captured by the intensity of each spot produced by the radioactive isotopes. The pixels per spot were counted with an Arrayguage, and were exported to Microsoft Excel The data were normalized by the Z transformation, and the comparisons were peformed on the Z-ratio values calculated. Results : The expressions of 15 genes, including integrin linked kinase (ILK), CDC28 protein kinase 2, Spry 2, and ERK 3, were increased with the Z-ratio values of over 2.0 for the cervix cancer tissues compared to those for the normal controls. Those genes were involved In cell growth and proliferation, cell cycle control, or signal transduction. The expressions of the other 6 genes, Including G protein coupled receptor kinase 5, were decreased with the Z-ratio values of below -2.0. After the radiation thorapy, most of the genes, with a previously Increase expressions, represented the decreased expression profiles, and the genes, with the Z-ratio values of over 2.0, were cyclic nucleotlde gated channel and 3 Expressed sequence tags (EST). In the concurrent chemo-radiation group, the genes involved in cell growth and proliferation, cell cycle control, and signal transduction were shown to have increased expressions compared to the radiation therapy alone group. The expressions of genes involved in anglogenesis (angiopoietln-2), immune reactions (formyl peptide receptor-iike 1), and DNA repair (CAMP phosphodiesterase) were increased, however, the expression of gene involved In apoptosls (death associated protein kinase) was decreased. Conclusion : The different kinds of genes involved in the development and progression of cervical cancer were identified with the CDNA microarray, and the proposed theory is that the proliferation signal stalls with ILK, and is amplified with Spry 2 and MAPK signaling, and the cellular mitoses are Increased with the increased expression oi Cdc 2 and cell division kinases. After the radiation therapy, the expression profiles demonstrated 4he evidence of the decreased cancer cell proliferation. There was no sigificant difference in the morphological findings of cell death between the radiation therapy aione and the chemo-radiation groups In the second time biopsy specimen, however, the gene expression profiles were markedly different, and the mechanism at the molecular level needs further study.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.110-122
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• 2010

The emergence of the field of organizational buying behavior in the mid-1960’s with the publication of Industrial Buying and Creative Marketing (1967) set the stage for a new paradigm of thinking about how business was conducted in markets other than those serving ultimate consumers. Whether it is "industrial marketing" or "business-to-business marketing" (B-to-B), organizational buying behavior remains the core differentiating characteristic of this domain of marketing. This paper explores the impact of several dynamic factors that have influenced how organizations relate to one another in a rapidly increasing interdependence, which in turn can impact organizational buying behavior. The paper also raises the question of whether or not the major conceptual models of organizational buying behavior in an interdependent world are still relevant to guide research and managerial thinking, in this dynamic business environment. The paper is structured to explore three questions related to organizational interdependencies: 1. What are the factors and trends driving the emergence of organizational interdependencies? 2. Will the major conceptual models of organizational buying behavior that have developed over the past half century be applicable in a world of interdependent organizations? 3. What are the implications of organizational interdependencies on the research and practice of organizational buying behavior? Consideration of the factors and trends driving organizational interdependencies revealed five critical drivers in the relationships among organizations that can impact their purchasing behavior: Accelerating Globalization, Flattening Networks of Organizations, Disrupting Value Chains, Intensifying Government Involvement, and Continuously Fragmenting Customer Needs. These five interlinked drivers of interdependency and their underlying technological advances can alter the relationships within and among organizations that buy products and services to remain competitive in their markets. Viewed in the context of a customer driven marketing strategy, these forces affect three levels of strategy development: (1) evolving customer needs, (2) the resulting product/service/solution offerings to meet these needs, and (3) the organization competencies and processes required to develop and implement the offerings to meet needs. The five drivers of interdependency among organizations do not necessarily operate independently in their impact on how organizations buy. They can interact with each other and become even more potent in their impact on organizational buying behavior. For example, accelerating globalization may influence the emergence of additional networks that further disrupt traditional value chain relationships, thereby changing how organizations purchase products and services. Increased government involvement in business operations in one country may increase costs of doing business and therefore drive firms to seek low cost sources in emerging markets in other countries. This can reduce employment opportunitiesn one country and increase them in another, further accelerating the pace of globalization. The second major question in the paper is what impact these drivers of interdependencies have had on the core conceptual models of organizational buying behavior. Consider the three enduring conceptual models developed in the Industrial Buying and Creative Marketing and Organizational Buying Behavior books: the organizational buying process, the buying center, and the buying situation. A review of these core models of organizational buying behavior, as originally conceptualized, shows they are still valid and not likely to change with the increasingly intense drivers of interdependency among organizations. What will change however is the way in which buyers and sellers interact under conditions of interdependency. For example, increased interdependencies can lead to increased opportunities for collaboration as well as conflict between buying and selling organizations, thereby changing aspects of the buying process. In addition, the importance of communication processes between and among organizations will increase as the role of trust becomes an important criterion for a successful buying relationship. The third question in the paper explored consequences and implications of these interdependencies on organizational buying behavior for practice and research. The following are considered in the paper: the need to increase understanding of network influences on organizational buying behavior, the need to increase understanding of the role of trust and value among organizational participants, the need to improve understanding of how to manage organizational buying in networked environments, the need to increase understanding of customer needs in the value network, and the need to increase understanding of the impact of emerging new business models on organizational buying behavior. In many ways, these needs deriving from increased organizational interdependencies are an extension of the conceptual tradition in organizational buying behavior. In 1977, Nicosia and Wind suggested a focus on inter-organizational over intra-organizational perspectives, a trend that has received considerable momentum since the 1990's. Likewise for managers to survive in an increasingly interdependent world, they will need to better understand the complexities of how organizations relate to one another. The transition from an inter-organizational to an interdependent perspective has begun, and must continue so as to develop an improved understanding of these important relationships. A shift to such an interdependent network perspective may require many academicians and practitioners to fundamentally challenge and change the mental models underlying their business and organizational buying behavior models. The focus can no longer be only on the dyadic relations of the buying organization and the selling organization but should involve all the related members of the network, including the network of customers, developers, and other suppliers and intermediaries. Consider for example the numerous partner networks initiated by SAP which involves over 9000 companies and over a million participants. This evolving, complex, and uncertain reality of interdependencies and dynamic networks requires reconsideration of how purchase decisions are made; as a result they should be the focus of the next phase of research and theory building among academics and the focus of practical models and experiments undertaken by practitioners. The hope is that such research will take place, not in the isolation of the ivory tower, nor in the confines of the business world, but rather, by increased collaboration of academics and practitioners. In conclusion, the consideration of increased interdependence among organizations revealed the continued relevance of the fundamental models of organizational buying behavior. However to increase the value of these models in an interdependent world, academics and practitioners should improve their understanding of (1) network influences, (2) how to better manage these influences, (3) the role of trust and value among organizational participants, (4) the evolution of customer needs in the value network, and (5) the impact of emerging new business models on organizational buying behavior. To accomplish this, greater collaboration between industry and academia is needed to advance our understanding of organizational buying behavior in an interdependent world.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.89-97
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• 2010

This study researches the effects of common features on a no-choice option with respect to regulatory focus theory. The primary interest is in three factors and their interrelationship: common features, no-choice option, and regulatory focus. Prior studies have compiled vast body of research in these areas. First, the "common features effect" has been observed bymany noted marketing researchers. Tversky (1972) proposed the seminal theory, the EBA model: elimination by aspect. According to this theory, consumers are prone to focus only on unique features during comparison processing, thereby dismissing any common features as redundant information. Recently, however, more provocative ideas have attacked the EBA model by asserting that common features really do affect consumer judgment. Chernev (1997) first reported that adding common features mitigates the choice gap because of the increasing perception of similarity among alternatives. Later, however, Chernev (2001) published a critically developed study against his prior perspective with the proposition that common features may be a cognitive load to consumers, and thus consumers are possible that they are prone to prefer the heuristic processing to the systematic processing. This tends to bring one question to the forefront: Do "common features" affect consumer choice? If so, what are the concrete effects? This study tries to answer the question with respect to the "no-choice" option and regulatory focus. Second, some researchers hold that the no-choice option is another best alternative of consumers, who are likely to avoid having to choose in the context of knotty trade-off settings or mental conflicts. Hope for the future also may increase the no-choice option in the context of optimism or the expectancy of a more satisfactory alternative appearing later. Other issues reported in this domain are time pressure, consumer confidence, and alternative numbers (Dhar and Nowlis 1999; Lin and Wu 2005; Zakay and Tsal 1993). This study casts the no-choice option in yet another perspective: the interactive effects between common features and regulatory focus. Third, "regulatory focus theory" is a very popular theme in recent marketing research. It suggests that consumers have two focal goals facing each other: promotion vs. prevention. A promotion focus deals with the concepts of hope, inspiration, achievement, or gain, whereas prevention focus involves duty, responsibility, safety, or loss-aversion. Thus, while consumers with a promotion focus tend to take risks for gain, the same does not hold true for a prevention focus. Regulatory focus theory predicts consumers' emotions, creativity, attitudes, memory, performance, and judgment, as documented in a vast field of marketing and psychology articles. The perspective of the current study in exploring consumer choice and common features is a somewhat creative viewpoint in the area of regulatory focus. These reviews inspire this study of the interaction possibility between regulatory focus and common features with a no-choice option. Specifically, adding common features rather than omitting them may increase the no-choice option ratio in the choice setting only to prevention-focused consumers, but vice versa to promotion-focused consumers. The reasoning is that when prevention-focused consumers come in contact with common features, they may perceive higher similarity among the alternatives. This conflict among similar options would increase the no-choice ratio. Promotion-focused consumers, however, are possible that they perceive common features as a cue of confirmation bias. And thus their confirmation processing would make their prior preference more robust, then the no-choice ratio may shrink. This logic is verified in two experiments. The first is a $2{\times}2$ between-subject design (whether common features or not X regulatory focus) using a digital cameras as the relevant stimulus-a product very familiar to young subjects. Specifically, the regulatory focus variable is median split through a measure of eleven items. Common features included zoom, weight, memory, and battery, whereas the other two attributes (pixel and price) were unique features. Results supported our hypothesis that adding common features enhanced the no-choice ratio only to prevention-focus consumers, not to those with a promotion focus. These results confirm our hypothesis - the interactive effects between a regulatory focus and the common features. Prior research had suggested that including common features had a effect on consumer choice, but this study shows that common features affect choice by consumer segmentation. The second experiment was used to replicate the results of the first experiment. This experimental study is equal to the prior except only two - priming manipulation and another stimulus. For the promotion focus condition, subjects had to write an essay using words such as profit, inspiration, pleasure, achievement, development, hedonic, change, pursuit, etc. For prevention, however, they had to use the words persistence, safety, protection, aversion, loss, responsibility, stability etc. The room for rent had common features (sunshine, facility, ventilation) and unique features (distance time and building state). These attributes implied various levels and valence for replication of the prior experiment. Our hypothesis was supported repeatedly in the results, and the interaction effects were significant between regulatory focus and common features. Thus, these studies showed the dual effects of common features on consumer choice for a no-choice option. Adding common features may enhance or mitigate no-choice, contradictory as it may sound. Under a prevention focus, adding common features is likely to enhance the no-choice ratio because of increasing mental conflict; under the promotion focus, it is prone to shrink the ratio perhaps because of a "confirmation bias." The research has practical and theoretical implications for marketers, who may need to consider common features carefully in a practical display context according to consumer segmentation (i.e., promotion vs. prevention focus.) Theoretically, the results suggest some meaningful moderator variable between common features and no-choice in that the effect on no-choice option is partly dependent on a regulatory focus. This variable corresponds not only to a chronic perspective but also a situational perspective in our hypothesis domain. Finally, in light of some shortcomings in the research, such as overlooked attribute importance, low ratio of no-choice, or the external validity issue, we hope it influences future studies to explore the little-known world of the "no-choice option."

• Korean Journal of Agricultural Science
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• v.6 no.2
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• pp.166-184
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• 1979

In order to prevent the water loss in the irrigation canal constructed on the sandy gravel layer or on the other highly permeable ground layer, lining has been practiced. Many studies have been done so far on the lining method to prevent the water loss in the irrigation canal and recently studies on the lining with plastic film or polyethylene film were also reported. However, the plastic film or polyethylene film has low strength and is liable to break, and water loss from pin hole caused by contacting with sand or gravel is highly predicted. This study was then conducted to find proper lining and buring method in canal construction of polypropylene mat after coated with vinyl, as one way to overcome the shortcoming frequently observed when plastic or usual polyehtylene film were used. Eventhough rather longer periods of experiments are needed to attain reliable and accurate results on the variation of durability, the durability of asphalt coated area, or on the damage due to freeze after burial or exposure of polypropylene mat, the experiemental results obtained during one year of period are summarized as follows: 1. The curvature at the area between canal bottom and side slope had increased stability and saved consruction cost. The relationship among the variation of curvature, the reduction of polypropylene mat and the reduced amount of soil cutting at each side slope was presented in Fig. 7 through 9. 2. The depth of covering material to protect polypropylene mat was desired to be over 30cm, considering the water depth, side slope, canal cleaning practices, traffic, or back pressure of irrigation period. 3. In order to increase the canal stability and to prevent slope erosion, sandy soil was required, to be placed under ground, and coarse gravel should cover the surface area of canal. 4. The studies on the stability of side slope in the canal should consider the passive area on the bottom and the slope should be about 1 to 2, considering the slope stability, allowable velocity and tractive force. 5. When compared with earth lining, the lining with polypropylene mat coated with vinyl was responsible to save 28% and 37% of canal lining cost, when the soil carrying distances were 500 and 700m. respectively. 6. The water interception was almost completely attained when the polypropylene mat coated with vinyl was used for lining. But further studies were assumed to be necessary for the use of asphalt since the strength of polypropylene mat connected with asphalt will vary with duration.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.