• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.1-9
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• 2018

The purpose of this study was to identify differences of perceptions for gardens and parks between experts and the general public concerning several aspects including scope, scale, publicity, artistic and scientific nature, main materials, practicality and aesthetics, executive and management systems as well as legal understanding of garden and park. The properties of garden and park were derived through literature research, and the concept, similarity, and difference of gardens and the parks were recognized by the experts and the public viewpoint was clarified by questionnaire. As for the difference in the scope of the gardens and the parks, the expert group recognized it more widely than the general public. In general, the space recognized as a garden was the rooftop green space, and urban forests were recognized as a park. In addition, the general public recognized urban forests as gardens the same as they recognized parks, and the distinction was unclear. In the expert group, the perception that gardens were small and the parks were large was more prevalent. It was generally recognized that gardens were private spaces and the parks were public spaces. In the expert group, the gardens were more personal and the parks were more apparent to the public. In the general population, functional and scientific aspects rather than artistic creativity in both gardens and parks. In addition, both the general public and experts found that parks are more complex than gardens. The garden was centered on plant material, and the park was recognized as a center where the sculptural facilities were centered, or the plant material and the sculptural facilities were properly balanced. To the experts the view of the gardens was positive. Expert groups emphasized the aesthetics of the garden, and the parks were more practical, and the general population showed similar perceptions of utility and aesthetics when comparing gardens and parks. In addition, the utility of gardens in the general publics is more emphasized than the aesthetics of the park. Regarding the executive system the park was recognized as the public sector, and the difference was larger in the expert group. As for the management system, both experts and the general public perceive the management of the park or the garden to be carried out by the supporting organization, and it is necessary to discuss the diversification of the management subject. It is found that there is a certain difference in recognition with the mixture of concepts, and there is still a big difference in legal system and perception.

• Journal of Soil and Groundwater Environment
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• v.11 no.6
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• pp.69-75
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• 2006

Improper disposal of petroleum and spills from underground storage tanks have created large areas with highly toxic contamination of the soil and groundwater. Methyl tert-butyl ether (MTBE) is widely used as a fuel additive because of its advantageous properties of increasing the octane value and reducing carbon monoxide and hydrocarbon exhausts. However, MTBE is categorized as a possible human carcinogen. This research investigated the Modified Photo-Fenton system which is based on the Modified Fenton reaction and UV light irradiation. The Modified Fenton reaction is effective for MTBE degradation near a neutral pH, using the ferric ion complex composed of a ferric ion and environmentally friendly organic chelating agents. This research was intended to treat high concentrations of MTBE; thus, 1,000 mg/L MTBE was chosen. The objectives of this research are to find the optimal reaction conditions and to elucidate the kinetic and mechanism of MTBE degradation by the Modified Photo-Fenton reaction. Based on the results of experiments, citrate was chosen among eight chelating agents as the candidate for the Modified Photo-Fenton reaction because it has a relatively higher final pH and MTBE removal efficiency than the others, and it has a relatively low toxicity and is rapidly biodegradable. MTBE degradation was found to follow pseudo-first-order kinetics. Under the optimum conditions, [$Fe^{3+}$] : [Citrate] = 1 mM: 4 mM, 3% $H_2O_2$, 17.4 kWh/L UV dose, and initial pH 6.0, the 1000 ppm MTBE was degraded by 86.75% within 6 hours and 99.99% within 16 hours. The final pH value was 6.02. The degradation mechanism of MTBE by the Modified Photo-Fenton Reaction included two diverse pathways and tert-butyl formate (TBF) was identified to be the major degradation intermediate. Attributed to the high solubility, stability, and reactivity of the ferric-citrate complexes in the near neutral condition, this Modified Photo-Fenton reaction is a promising treatment process for high concentrations of MTBE under or near a neutral pH.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Economic and Environmental Geology
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• v.56 no.2
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• pp.125-138
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• 2023

Most of previous cesium (Cs) sorbents have limitations on the treatment in the large-scale water system having low Cs concentration and high ion strength. In this study, the new Cs sorbent that is eco-friendly and has a high Cs removal efficiency was developed by improving the coal mine drainage treated sludge (hereafter 'CMDS') with the addition of Na and S. The sludge produced through the treatment process for the mine drainage originating from the abandoned coal mine was used as the primary material for developing the new Cs sorbent because of its high Ca and Fe contents. The CMDS was improved by adding Na and S during the heat treatment process (hereafter 'Na-S-CMDS' for the developed sorbent in this study). Laboratory experiments and the sorption model studies were performed to evaluate the Cs sorption capacity and to understand the Cs sorption mechanisms of the Na-S-CMDS. The physicochemical and mineralogical properties of the Na-S-CMDS were also investigated through various analyses, such as XRF, XRD, SEM/EDS, XPS, etc. From results of batch sorption experiments, the Na-S-CMDS showed the fast sorption rate (in equilibrium within few hours) and the very high Cs removal efficiency (> 90.0%) even at the low Cs concentration in solution (< 0.5 mg/L). The experimental results were well fitted to the Langmuir isotherm model, suggesting the mostly monolayer coverage sorption of the Cs on the Na-S-CMDS. The Cs sorption kinetic model studies supported that the Cs sorption tendency of the Na-S-CMDS was similar to the pseudo-second-order model curve and more complicated chemical sorption process could occur rather than the simple physical adsorption. Results of XRF and XRD analyses for the Na-S-CMDS after the Cs sorption showed that the Na content clearly decreased in the Na-S-CMDS and the erdite (NaFeS₂·2(H₂O)) was disappeared, suggesting that the active ion exchange between Na⁺ and Cs⁺ occurred on the Na-S-CMDS during the Cs sorption process. From results of the XPS analysis, the strong interaction between Cs and S in Na-S-CMDS was investigated and the high Cs sorption capacity was resulted from the binding between Cs and S (or S-complex). Results from this study supported that the Na-S-CMDS has an outstanding potential to remove the Cs from radioactive contaminated water systems such as seawater and groundwater, which have high ion strength but low Cs concentration.

• 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 Korean Society of Environmental Engineers
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• v.33 no.4
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• pp.289-299
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• 2011

We studied the characteristics of pollutant concentrations in 9 streams that flow into Shihwa Lake in order to provide the scientific data for effective implementation of total pollution loads management system (TPLMS) of the Lake. Suspended solid (SS), chemical oxygen demand (COD), dissolved nutrients ($NO_2$, $NO_3$, $NH_4$, $PO_4$ and $SiO_2$), total phosphorus (TP) and total nitrogen (TN) in stream water from industrial complexes, urban and agricultural regions were determined. Pollutant concentrations in December were higher than that in other sampling periods. COD concentration from industrial complex region with average of 12.6 mg/L was 2 times higher those from urban region (6.6 mg/L) and agricultural region (5.9 mg/L). TP concentration from industrial region also showed higher concentration than other regions. TN concentration in stream water was 5.89 mg/L for industrial region, 3.02 mg/L for urban region and 5.27 mg/L for agricultural region, respectively, suggesting inflow of TN due to fertilizer usage in agricultural field. Relative percentage of nitrogen compounds in TN follows the sequence: $NH_4$ (35.1%) > $NO_2$ (20.0%) > DON (22.8%) > PON (8.9%) > $NO_2$ (3.2%). Concentrations of dissolved nutrients, TP and TN in stream water were 3.2~37.2 times higher than that in Shihwa Lake seawater, therefore large amount of pollutants may be directly entered into Shihwa Lake without any treatment. For Gunja stream of industrial region, pollutants at midstream showed relatively higher concentration compared to upstream and downstream. It is necessary to manage the illegal discharging of sewage and waste water. Our results provide valuable informations on the estimation and reduction of total pollutant loads in the process of establishing adequately strategic and implemental plan of Shihwa Lake TPLMS.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• The Korean Journal of Petroleum Geology
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• v.5 no.1_2 s.6
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• pp.9-15
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• 1997

The study area is located in the Central Bransfield Basin, Antarctica. To analyze the morphology of seafloor, structure of basement, and seismic stratigraphy of the sedimentary layers, we have acquired, processed, and interpreted the multi-channel seismic data. The northwest-southeastern back-arc extension dramatically changes seafloor morphology, volcanic and fault distribution, and basin structure along the spreading ridges. The northern continental shelf shows a narrow, steep topography. In contrast, the continental shelf or slope in the south, which is connected to the Antarctic Peninsula, has a gentle gradient. Volcanic activities resulted in the formation of large volcanos and basement highs near the spreading center, and small-scale volcanic diapirs on the shelf. A very long, continuous normal fault characterizes the northern shelf, whereas several basinward synthetic faults probably detach into the master fault in the south. Four transfer faults, the northwest-southeastern deep-parallel structures, controlled the complex distributions of the volcanos, normal faults, depocenters, and possibly hydrocarbon provinces in the study area. They have also deformed the basement structure and depositional pattern. Even though the Bransfield Basin was believed to be formed in the Late Cenozoic (about 4 Ma), the hydrocarbon potential may be very high due to thick sediment accumulation, high organic contents, high heat flow resulted from the active tectonics, and adequate traps.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.25-41
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• 2014

Recently, the number of social media users has increased rapidly because of the prevalence of smart devices. As a result, the amount of real-time data has been increasing exponentially, which, in turn, is generating more interest in using such data to create added value. For instance, several attempts are being made to analyze the relevant search keywords that are frequently used on new portal sites and the words that are regularly mentioned on various social media in order to identify social issues. The technique of "topic analysis" is employed in order to identify topics and themes from a large amount of text documents. As one of the most prevalent applications of topic analysis, the technique of issue tracking investigates changes in the social issues that are identified through topic analysis. Currently, traditional issue tracking is conducted by identifying the main topics of documents that cover an entire period at the same time and analyzing the occurrence of each topic by the period of occurrence. However, this traditional issue tracking approach has two limitations. First, when a new period is included, topic analysis must be repeated for all the documents of the entire period, rather than being conducted only on the new documents of the added period. This creates practical limitations in the form of significant time and cost burdens. Therefore, this traditional approach is difficult to apply in most applications that need to perform an analysis on the additional period. Second, the issue is not only generated and terminated constantly, but also one issue can sometimes be distributed into several issues or multiple issues can be integrated into one single issue. In other words, each issue is characterized by a life cycle that consists of the stages of creation, transition (merging and segmentation), and termination. The existing issue tracking methods do not address the connection and effect relationship between these issues. The purpose of this study is to overcome the two limitations of the existing issue tracking method, one being the limitation regarding the analysis method and the other being the limitation involving the lack of consideration of the changeability of the issues. Let us assume that we perform multiple topic analysis for each multiple period. Then it is essential to map issues of different periods in order to trace trend of issues. However, it is not easy to discover connection between issues of different periods because the issues derived for each period mutually contain heterogeneity. In this study, to overcome these limitations without having to analyze the entire period's documents simultaneously, the analysis can be performed independently for each period. In addition, we performed issue mapping to link the identified issues of each period. An integrated approach on each details period was presented, and the issue flow of the entire integrated period was depicted in this study. Thus, as the entire process of the issue life cycle, including the stages of creation, transition (merging and segmentation), and extinction, is identified and examined systematically, the changeability of the issues was analyzed in this study. The proposed methodology is highly efficient in terms of time and cost, as it sufficiently considered the changeability of the issues. Further, the results of this study can be used to adapt the methodology to a practical situation. By applying the proposed methodology to actual Internet news, the potential practical applications of the proposed methodology are analyzed. Consequently, the proposed methodology was able to extend the period of the analysis and it could follow the course of progress of each issue's life cycle. Further, this methodology can facilitate a clearer understanding of complex social phenomena using topic analysis.