• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of the Korean Society of Groundwater Environment
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• v.7 no.3
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• pp.103-115
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• 2000

The formation of brown-colored precipitates is one of the serious problems frequently encountered in the development and supply of groundwater in Korea, because by it the water exceeds the drinking water standard in terms of color. taste. turbidity and dissolved iron concentration and of often results in scaling problem within the water supplying system. In groundwaters from the Pajoo area, brown precipitates are typically formed in a few hours after pumping-out. In this paper we examine the process of the brown precipitates' formation using the equilibrium thermodynamic and kinetic approaches, in order to understand the origin and geochemical pathway of the generation of turbidity in groundwater. The results of this study are used to suggest not only the proper pumping technique to minimize the formation of precipitates but also the optimal design of water treatment methods to improve the water quality. The bed-rock groundwater in the Pajoo area belongs to the Ca-$HCO_3$type that was evolved through water/rock (gneiss) interaction. Based on SEM-EDS and XRD analyses, the precipitates are identified as an amorphous, Fe-bearing oxides or hydroxides. By the use of multi-step filtration with pore sizes of 6, 4, 1, 0.45 and 0.2 $\mu\textrm{m}$, the precipitates mostly fall in the colloidal size (1 to 0.45 $\mu\textrm{m}$) but are concentrated (about 81%) in the range of 1 to 6 $\mu\textrm{m}$in teams of mass (weight) distribution. Large amounts of dissolved iron were possibly originated from dissolution of clinochlore in cataclasite which contains high amounts of Fe (up to 3 wt.%). The calculation of saturation index (using a computer code PHREEQC), as well as the examination of pH-Eh stability relations, also indicate that the final precipitates are Fe-oxy-hydroxide that is formed by the change of water chemistry (mainly, oxidation) due to the exposure to oxygen during the pumping-out of Fe(II)-bearing, reduced groundwater. After pumping-out, the groundwater shows the progressive decreases of pH, DO and alkalinity with elapsed time. However, turbidity increases and then decreases with time. The decrease of dissolved Fe concentration as a function of elapsed time after pumping-out is expressed as a regression equation Fe(II)=10.l exp(-0.0009t). The oxidation reaction due to the influx of free oxygen during the pumping and storage of groundwater results in the formation of brown precipitates, which is dependent on time, $Po_2$and pH. In order to obtain drinkable water quality, therefore, the precipitates should be removed by filtering after the stepwise storage and aeration in tanks with sufficient volume for sufficient time. Particle size distribution data also suggest that step-wise filtration would be cost-effective. To minimize the scaling within wells, the continued (if possible) pumping within the optimum pumping rate is recommended because this technique will be most effective for minimizing the mixing between deep Fe(II)-rich water and shallow $O_2$-rich water. The simultaneous pumping of shallow $O_2$-rich water in different wells is also recommended.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Korean Journal of Heritage: History & Science
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• v.44 no.4
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• pp.172-205
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• 2011

Go bears significant meanings in terms of cultural and entertaining functions in Asia Eastern such as China and Japan. Beyond the mere entertaining level, it produces philosophical and mythic discourse as well. As a part of effort to seek an identity of Korean traditional garden culture, this study traced back to find meanings of rock-go-board and taste for the arts which ancestors pursued in playing Go game, through analysis and interpretation of correlation among origin of place name, nearby scenery, carved letters and vicinal handed-down place name. At the same time, their position, shape and location types were interpreted through comprehensive research and analysis of stone-go-boards including rock-go-board. Particularly, it focused on the rock names related to Sundoism(仙道) Ideal world, fixed due to a connection between traces of Sundoism and places in a folk etymology. Series of this work is to highlight features of the immortal sceneries, one of traditional landscaping ideals, by understanding place identity and scenic features of where the rock-go-boards are carved. These works are expected to become foundation for promotion and preservation of the traditional landscaping remains. The contents of this study could be summarized as follows; First, round stone and square board for round sky and angled land, black and white color for harmony of yin and yang and 361paths for rotating sky are symbols projecting order of universe. Sayings of Gyuljungjirak(橘中之樂), Sangsansaho(商山四皓), Nangagosa(爛柯故事) formed based on the idea of eternity stand for union of sky and sun. It indicates Go game which matches life and nature spatiotemporally and elegant taste for arts pursuing beauty and leisure. Second, the stone-go-boards found through this research, are 18 in total. 3 of those(16.1%), Gangjin Weolnamsaji, Yangsan Sohanjeong and Banryongdae ones were classified into movable Seokguk and 15(83.9%) including Banghakdong were turned out to be non-movable rock-go-boards carved on natural rocks. Third, upon the result of materializing location types of rock-go-boards, 15 are mountain stream type(83.9%) and 3 are rock peak type(16.1%). Among those, the one at Sobaeksam Sinseonbong is located at the highest place(1,389m). Considering the fact that all of 15 rock-go-boards were found at mountainous areas lower than 500m, it is recognizable that where the Go-boards are the parts of the living space, not far from secular world. Fourth, there are 7 Sunjang(巡將) Go with 17 Hwajeoms(花點), which is a traditional Go board type, but their existences, numbers and shapes of Hwajeom appear variously. Based on the fact, it is recognizable that culture of making go-board had been handed down for an extended period of time. Among the studied rock-goboards, the biggest one was Muju Sasunam[$80(82)cm{\times}80(82)cm$] while the smallest one was Yangsan Sohandjeong Seokguk ($40cm{\times}40cm$). The dimension of length and breadth are both $49cm{\times}48cm$ on average, which is realistic size for actual Go play. Fifth, the biggest bed rock, an under-masonry with carved Go-board on it, was one in Muju Sasunam[$8.7m{\times}7.5m(65.25m^2)$], followed by ones in Hoengseong Chuiseok[$7.8m{\times}6.3m(49.14m^2$] and Goisan Sungukam[$6.7m{\times}5.7m(37.14m^2)$]. Meanwhile, the smallest rock-go-board was turned out to be one in Seoul Banghak-dong. There was no consistency in directions of the Go-boards, which gives a hint that geographical features and sceneries of locations were considered first and then these were carved toward an optimal direction corresponding to the conditions. Sixth, rock-go-boards were all located in valleys and peaks of mountains with breathtaking scenery. It seems closely related to ancestors' taste for arts. Particularly, rock-go-boards are apprehended as facilities related to taste for arts for having leisure in many mountains and big streams under the idea of union of sky and human as a primitive communal line. Go became a medium of hermits, which is a traditional image of Go-game, and symbol of amusement and entertainment with the idea that Go is an essence of scholar culture enabling to reach the Tao of turning back to nature. Seventh, the further ancient time going back to, the more dreamlike the Go-boards are. It is an evident for that Sundoism, which used to be unacceptable once, became more visible and realistic. Considering the high relation between rock-go-boards and Sundoism relevant names such as Sundoism peak in Danyang Sobaeksan, 4 hermits rock in Muju and Sundoism hermit rock in Jangsu, Sundoism hermit rocks and rock-go-boards are sceneries and observation spots to express a communication of worship and longing for Sundoism. Eighth, 3 elements-physical environment such as location type of the rock-go-boards, human activities concentrated on 8 sceneries and Dongcheongugok(洞天九曲) setup and relevancy to Confucian scholars, as well as 'Sangsansaho' motif and 'Nangagosa' symbolic meaning were used as interpretation tools in order to judge the place identity. Upon the result, spatial investigation is required with respect to Sunyoodongcheon(仙遊洞天) concept based on enjoyment to unify with the nature rather than Dongcheongugok concept of neo-Confucian, for Dongcheon and Dongmoon(洞門) motives carved around the rock-go-boards. Generally, places where mountain stream type rock-go-boards were formed were hermit spaces of Confucianism or Sundoism. They are considered to have compromised one other with the change of times. Particularly, in the rock-go-board at the mountain peak, sublimity-oriented advent of Sundoism is considered as a significant factor to control place identity. Ninth, including where the rock-go-boards were established, the vicinal areas are well-known as parts of Dongcheongugok and Palkyung(八景) mostly. In addition, many of Sundoism relevant expressions were discovered even in the neighboring carvings written by scholars and nobility, which means sophisticated taste based on longing for Sundoism world played a significant role in making go-board. The rock-go-board is an integration of cultural phenomena naturally managed by seclusion of scholars in the Joseon Dynasty as well as remains and essence of Korean traditional landscaping. Some rock-go-boards out of 17 discovered in South Korea, including ones in Sobaeksan Sinsunbong, Banghak-dong, Chungju Gongili, Muju Sasunam, Yangsan Eogokdong Banryongdae Seokguk, are damaged such as cracks in rocks or fainted lines by hardships of time and hand stains. Worse yet, in case of Eunyang Bangudae Jipcheongjeong board, it is very difficult to identify the shape due to being buried. Rock-go-boards are valuable sculptures in terms of cultural asset and artwork since they reflect ancestors' love for nature and longing for Sundoism world. Therefore, they should be maintained properly with right preservation method. Not only rock-boards itself but also peripheral places are excellent cultural heritages and crucial cultural assets. In addition, vicinal sceneries of where rock-goboards and pavilion spots are the representative remains of embracing prototype of Korean traditional landscaping and major parts of cultural properties.