• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Korean Journal of Soil Science and Fertilizer
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• v.21 no.3
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• pp.316-338
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• 1988

To identify the physiological phenomena on the sudden wilting of japonica/indica crossed varieties, Pot experiment was carried out under the heavy N application with various levels of potassium in Japan. The results obtained are as follows. 1. Sudden wilting was occurred in both varieties used, Yushin and Milyang 23. The former showed a higher degree than the latter. 2. Sudden wilting was occurred into two types, one at early ripening stage and the other at late ripening stage. The former type was found in the field with low potassium supply and the latter was seemed to be related to varietal wilting tolerence. 3. By the investigation of concerning the effective tillering rate and the change of dry weight of each organ at the heading stage, it was inferred that the growth status from young panicle formation stage to heading stage were related to sudden wilting tolerence. 4. Manganese content at heading stage, ratio of Fe/Mn and Fe. Fe/Mn in stern at late ripening stage and $K_2$ O/N ratio of stem at harvesting stage were recognized as the specific factors in connection with sudden wilting. Mn content in the sudden wilting rice plant was already in creased remarkably at heading stage. In relation to root age and absoption characteristics of Mn, the senility of root before heading stage was inferred as the cause of increase the value of Fe/Mn or Fe. Fe/Mn. 5. The $K_2$ O/N ratio of culm at harvesting stage was lower in upper node than lower node in relation to sudden wilting. And it was well accordance with the fact that the symptoms of sudden wilting proceeded from upper leaf to lower leaf. These phenomenon was different from the usual one that the effect of potassium deficiency was more remarkable in lower node than upper node. 6. All varieties which have a condition of potassium deficiency have a high degree of nitrogen content of leaves at heading stage and the $K_2$ O/N ratio of each organ was low, Especialy, $K_2$ O/N ratio is much lower in sheath and culm than leaves.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.5 no.1
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• pp.1-35
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• 1969

Attempts were made to obtain the fundamental knowledge on the quantitative constitution status of leaves and stem of elongating node-part, and the relationships between these morphological characteristics along with the nitrogen contents of leaves and grain yield were examined varing application amounts of nitrogen in rice plant. I. The agronomic characteristics of leaves and nodes of elongation node-part (4-node parts from the top of stem) were observed at heading stage with 20 leading rice varieties of Kang Won district. The results are summarized as follows: 1. Leaf area magnitude of the flag and the fourth leaf was smaller than that of the second and the third with the average value of flag leaf 18.61 $cm^2$, the second leaf 21.84 $cm^2$, the third 21.52 $cm^2$ and the fourth 18.56 $cm^2$. The weight of leaf blade showed an isotonic tendency with the magnitude of leaf area with the value of the flag leaf 97.0 mg, the second leaf 117.1 mg, the third 115.4 mg, and the fourth 95.3 mg. The weight of each leaf sheath was remarkably larger at the higher node-part than at the lower node-part of the stem with the value of flag leaf sheath 176.3 mg, the second 163.7 mg, the third 163.4 mg and the fourth 123.9 mg. Accordingly, the total leaf weight of each part was larger at the second and the third leaf than at the first and the fourth. Total plant weight of each part (weight of leaf blade, leaf sheath, and culm) also was larger at the middle node-part. 2. Coefficients of variation for the varietal differences of the morphological characteristics of elongating node-part were 12.75% for the leaf area, 15.29% for the weight of leaf blade, 15.90%, for the weight of leaf sheath, 11.42% for the weight of internode, 15.45% for the leaf weight (leaf blade ＆ leaf sheath) and 13.24% for the straw weight. And these coefficient values of the most characteristics were, on the whole, smaller at the second and the third node-part than at the first and the fourth node-part, but the coefficient value of the internode weight was rather small at the third and fourth node-part. 3. Constitutional ratio of each plant organ to the total plant weight in term of dry matter weight (excluding head and root wight) was 39.2% for the leaf sheath, 34.2% for the culm, 26.6% for the leaf blade. And ocnstitutional ratio of leaf sheath in term of dry matter weight was larger at the higher position in contrast with that of culm. 4. Average weight ration of leaf blade to culm, leaf sheath to culm, leaf blades to sheath and the leaf blades to culm plus leaf sheath were 77.7 %, 114.5%, 67.9% and 36.2%, respectively. With regard to the position of the plant organ, the weight ratio of leaf blade to culm and that of leaf sheath to culm were larger at higher part in contrast with that of leaf blade to leaf sheath. 5. Generally, there founded deep relationships between grain yield and each morphological characteristics of plant organ of elongating node-part as follows; Correlation coefficient between total area of 4 leaves (from flag to the fourth leaf) and grain yield was ${\gamma}$=0.666$^{**}$ In regard to the position of leaves, correlation coefficient values of flag, the second, the third and the fourth leaf were ${\gamma}$=0.659$^{**}$, ${\gamma}$=0.609$^{**}$, ${\gamma}$=0.464$^{*}$ and ${\gamma}$=0.523$^{*}$, respectively. Correlation coefficient between total weight of leaf blades and the grain yield was ${\gamma}$=0.678$^{**}$. In regard to the position of leaves, that of flag leaf was ${\gamma}$=0.691$^{**}$, and ${\gamma}$=0.654$^{**}$ for the second leaf, ${\gamma}$=0.570$^{**}$ for the third, and ${\gamma}$=0.544$^{**}$ for the fourth. Correlation between the weight of leaves (blade weight plus sheath weight) and the grain yield showed similar values. In the relationship between plant weight and grain yield there also was significant correlation, but with highly significant value only for the first node-part. There appeared correlation between total weight of leaf sheath and grain yield with the value of ${\gamma}$=0.572$^{**}$ and in regard to the position of each leaf sheath the values were ${\gamma}$=0.623$^{**}$ for the flag leaf, ${\gamma}$=0.486$^{**}$ for the second leaf, ${\gamma}$=0.513$^{**}$ for the third, ${\gamma}$=0.450$^{**}$ for the fourth. However, there was no significant correlation between culm weight and grain yield. 6. With respect to in gain yield, varietal differences in magnitude of leaf area, weight of leaf blade, leaf weight per unit area, weight of leaf sheath, culm weight, total leaf and stem weight were larger in the case of high yielding varieties and decreased in accordance with decreasing yield. And this tendency also was shown in the varietal differences of magnitude of each part. Variation in magnitude of each part for the leaf area, weight of leaf blade, culm weight was significantly small in high yielding varieties compared to low yielding varieties. 7. Plant constitutional ratio of each organ of the elongating node-part in term of weight magnitnde varied to som extent according to varieties indicating leaf blade 27.6%, leaf sheath 39.5%, culm 32.9% in the case of high yielding varieties, leaf blade 25.5%, leaf sheath 38.1%, culm 36.4% in the case of low yielding varieties, and medium yielding varieties showed intermadiate values. 8. Far higher values of the weight ration of leaf blade to culm and leaf sheath to culm were given to the high yielding varieties compared to low yielding varieties. And medium yielding varieties showed intermadiate values. II. Effects of application rate of nitrogen on the morphological characteristics of the elongating node-part, nitrogen content of leaf blade, and their relation with the grain yield of the rice were observed with 3 rice varieties; Shin No.2, Shirogane, and Jinheung varying application amounts of nitrogen as 8kg, 12kg and 16kg per 10 are. 1. As for the variation of morphological magnitude s affected by the amounts of nitrogen application, total leaf area (4 leaves from the flag leaf) increased to 16.5% at 12kg N plot, and about 30% at 16kg N polt compared to 8kg N plot and total weight of leaf blade also increased to similar extent, respectively, in contrast with weight of leaf sheath increasing 4.9% and 7.8%, respectively. However, the weight of culm decreased to 1.5% and 11.2%at the 12kg N plot and 16kg N plot, respectively, and these decreasing rate was noted at the nodes of lower part. 2. As for the verietal differences in variation of morphological magnitude as affected by the amount of nitrogen fertilization, leaf area coefficient value of variation of the total leaf area was 15.40% for Shin No. 2, 12.87% for Shirogane, and 10.99% for Jinheung. With respect to the position of nodes, the largest variation of leaf blade magnitude was observed at the fourth for Shin No. 2, the second for Shirogan, and flag leaf for Jinheung. And there also was an isotonic varietal difference in the weight of leaf blade. Variation in total culm weight showed varietal differences with the coefficient value of 7.72% for Shin No.2, 12.11% for Shirogane, and 0.94% for Jinheung. There also was varietal differences in the variation according to the position of nodes. 3. Variation of each elongating node-part related to the fertilization amount decreased with the increase of fertilization amount in the items of leaf area, weight of leaf sheath, culm weight, but weight of leaf sheath varied more at heavier fertilization than at others. 4. Constitutional ratio of each organ excluding head also varied with fertilization amount; constitutional ratio of leaf blade increased much with the increasing amount of fertilization in contrast with the response of culm eight. However, constitutional ration of the weight of leaf sheath was not much affected. 5. Lower value of the ration of leaf blade to culm was given to the 8kg N per 10 are plot, and the ratio of leaf blade to leaf sheath decreased with the increasing amount of fertilization in contrast with the increase in the ratio of leaf sheath to culm. however, the ration of leaf blade to culm plus leaf sheath decreased. 6. With the increase of nitrogen fertilization, leaf area, weight of leaf blade and leaf sheath increased. Accordingly, grin yield also increased to some extent. It was noted that culm weight was changed inversely to the changes in grain yield, but the degree of this variation varied with varietal characteristics. 7. Nitrogen content of leaves at heading and fruiting stage varied with the fertilization amount, and average nitrogen content of leaves of the varieties used 2.19%, 2.49% and 2.74% at the plot of 8kg N, and 12kg N and 16kg N per 10 are, respectively, at heading time, and 0.80%, 0.92% and 1.03% at each plot at fruiting stage. Thus, nitrogen content of leaves increased much with the increasing amount of fertilization, and higher value was given to the leaves on the higher position of elongating node-part. 8. There also was variation of nitrogen content of leaves in accordance with the varieties. However higher grain yield was obtained from the plants retaining higher nitrogen content in leaves at heading or fruiting stage.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.