• Economic and Environmental Geology
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• v.57 no.1
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• pp.17-23
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• 2024

Ground penetrating radar (GPR) survey is a geophysical method that utilizes electromagnetic waves reflecting from a boundary where the electromagnetic property changes. As the frequency of the antenna is about 25 MHz ~ 1 GHz, it is effective to acquire high resolution images of underground pipe, artificial structure, underground cavity, and underground structure. In this study, we analyzed the change of signals reflected from the same underground objects according to the arrangement of transceiver antennas used in ground penetrating radar survey. The antenna used in the experiment was 200 MHz, and the survey was performed in the vertical direction across the sewer and the parallel direction along the sewer to the sewer buried under the road, respectively. A total of five antenna array methods were applied to the survey. The most used arrangement is when the transmitting and receiving antennas are all perpendicular to the survey line (PR-BD). The PR-BD arrangement is effective when the object underground is a horizontal reflector with an angle of less than 30°, such as the sewer under investigation. In this case study, it was confirmed that the transmitter and receiver antennas perpendicular to the survey line (PR-BD) are the most effective way to show the underground structure. In addition, in the case where the transmitting and receiving antennas are orthogonal to each other (XPOL), no specific reflected wave was observed in both experiments measured across or parallel to the sewer. Therefore, in the case of detecting undiscovered objects in the underground, the PR-BD array method in which the transmitting and receiving antennas are aligned in the direction perpendicular to the survey line taken as a reference and the XPOL method in which the transmitting and receiving antennas are orthogonal to each other are all used, it can be effective to apply both of the above arrangements after setting the direction to 45° and 135°.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.219-228
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• 2024

In this paper, we propose a method for constructing RDF knowledge graphs from objects in OOP. RML mapping has been the de-facto way of generating RDF graphs from heterogeneous data. However, the input to an RML mapping is limited to the data in files or databases. Our new RML implementation, designed to overcome the limit, has two differences compared to existing RML implementations. First, our implementation provides a new way to specify mapping rules in the form of special comments known as annotations in the source code. It is because existing works do not provide a means to refer to specific program elements to which the mapping rules will be applied. Second, our work provides mapping engine as a library, whereas the engines in existing studies runs in an independent process. Therefore, our mapping engine can be easily embedded in other applications to access in-memory objects to be mapped. In this system paper, we describe the proposed system in detail and present the results of RML test cases execution to confirm the usefulness of the system.

• Management & Information Systems Review
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• v.36 no.1
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• pp.115-141
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• 2017

This study is to find the key factors of the Internet of Things for development in public sector. In previous studies, it is said that Internet of Things can work digital system without human operation and gives a lot of outputs(information) users. Generally, people are a subject of operating digital system in traditional way, while people are an object on the internet of things. In other words, it is possible to work digital system with only networking from things to things. After all, it is reported that these advantages of the Internet of Things make possible to reduce social costs significantly in public sector. However, despite the strengths of the Internet of Things, there is a specific user acceptance of the technology factor for the Internet of Things rarely. It means that developing of the Internet of Things only focuses on the final purpose. If the focus on development meet this purpose, the user is ignored for the specific reason that using a technique. As a result of this, many users gradually decrease the continuous using of the Internet of Things. Thus, in this study, we need to find what critical factors should reflect to the Internet of Things in public sector. To find this result, there is no choice to use Technology Acceptance Model(TAM). Many researchers have proved that Technology Acceptance Model is valid through the four process in model introduction, confirmation, expansion and refinement from 1986 to 2003. The results of this study showed that the result explanatory power of Internet of Things in public sector is the most important factor affecting only perceived social usefulness and ease of use. Finally, it can be seen that the user has a positive attitude toward use, which has a positive effect on the intention to use continuously. The implications of this study are summarized as follows: When the public Internet of Things service is provided, it means that the user can easily understand the result, and when the person and the object communicate the result to each other, they should be able to communicate with each other. This means that a lot of user effort is needed to understand the outcome of the public Internet of Things being provided.

• Journal of Korean Philosophical Society
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• v.130
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• pp.305-335
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• 2014

Counseling is conducted through dialogue in relation to counselor and client. Therefore the philosophical counseling first must consider the circumstances, prescribe the main concepts and proceed to the specific methodology in order to be the practical study. The philosophical counseling includes the six necessary concepts-subjects, time, place, object, method, and purpose-because of its behavioral concepts. The subjects are counsellor and client, the place is where public institutions authorize officially for counseling, the time is when the two parties are meeting face to face, the object is the client's facing problems right now, the method is the philosophical assistance, and the purpose is to dissolve the client's problems. The client's facing problems here are the developmental tasks according to the developmental stages and the maladaptive behaviors related to the cognitive distortions appearing in the process. And the philosophical assistance methods are the types to make the facilitating environment and dispute the wrong thoughts and the irrational beliefs. However, the client's problems in counseling often appear in the causes combined between the cognitive elements and the emotive elements which are treated mainly in the psychological counseling. In that case, the way to solve the problems in the philosophical counseling should be applied to with the psychological methods in parallel or in regular succession. Therefore the six necessary concepts of the philosophical counseling are not the absolute meanings but the meanings in general. If so, the concept of the philosophical counseling can be defined as the process in which counselor and client meet face to face and dissolve the client's facing problems through mainly the philosophical methods with the counselor's assistance. If the main concepts of the philosophical counseling can be prescribed as mentioned above, post study needs to proceed to the specific methodology.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.5
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• pp.82-93
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• 2008

This research focused on the process in which 'the same scenery' is recognized and represented as 'a different landscape' to determine the symbols and meaning of the scenery and landscape included in real-scenery landscape paintings of the 18th-19th centuries. As a result of analyzing the visual points, the content and expressions of 25 real-scenery landscape paintings of Chong Seok Jeong(叢石亭), it can be seen that the transmission of a kind of semiotic landscape on the basis of a specific symbol was accomplished naturally through imitation and representation for the purpose of the expression of Chong Seok Jeong-like idealized scenery. This shows that the unique images of Chong Seok Jeong have long been passed down after taking root as a unique benchmark The meaningful symbol of 'a strange Saseonbong(四仙峰)', which is broken by the spray after rising high, and 'a pine forest' have both been transmitted as being in the manner of Chong Seok Jeong. This has been equipped with the stereo-type scene by being a collective symbolization as the psycho-scenes in memory element of Chong Seok Jeong. Through the pictures of both Gyeomjae(謙齋) and Danweon(檀園), the process by which a specific painter's pictures become acculturated is highly interesting. The scenery expressed in these pictures was clearly that of a landscape of which its particularly emotions and remembrances were repainted through the experience of several places and original sketches. This can be explained as the concept in which the image from 'a specific scenery' gained through actual experience, that is, a personal feeling, has been expressed. The picture that was expressed as a different figure even at the same visual point for the same scenery is the result that was redefined through the scenery subject's recognition. Also, the modification of the scenery object can be colorful through meditation and Sachu(邪推: guessing with wicked doubt). The scenery recognized newly through adoption, omission and emphasis, it is 'the specific scenery' in the heart and is a figure having been more similar to 'a landscape' if the objective life reproduction before being acculturated is a figure similar to the scenery. So, the concept looks like being very persuasive that 'the nature with objectivity captured sensuously' simply is the scenery, and that 'the subjective phenomenon having acquired the cultural nature by being introspected in the method of aesthetic nostalgia is a landscape'.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.877-895
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• 2014

It is common sense that providing specific odor can increase the video reality when video scene has an object having specific odor. However, people still do not know how to increase video reality and emotional immersion when there is no information on specific odor in the scene. So, present study explores how we improve video reality and immersion when the scene has no concrete odor information from some objects. Especially, this research focuses on diverse previous studies about matching between odor and color and then we expect providing odor can increase video reality if the odor is well-matched with the video's color. To do this, we collected 48 odors and investigated which color was well-matched with each odor. As a result, we get 5 odors which had clearly well-matched colors and decide ill-matched colors of those 5 odors as complementary colors of well-matched colors (Experiment 1). After that, we organize 3 conditions such as coloring image and video clip with well-matched color (color-odor match condition), coloring those with ill-matched color (color-odor mismatch condition), and coloring those with achromatic color by removing color saturation (color-odor neutral condition). Under each of these three conditions, image-odor matching, increment of reality with the odor, increment of immersion with the odor, and odor preference are asked (Experiment 2; 3). The results showed that the scores of all 4 questions in color-odor match condition were higher than color-odor mismatch condition and neutral condition. These results mean that providing matching odor with the scene's color in video is very effective to increase video reality and immersion. We expect experiencing better reality and immersion with olfactory information by adding various future research.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.193-215
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• 2016

In recent years, the proliferation of diverse social networking services has led users to use many mediums simultaneously depending on their individual purpose and taste. Besides, while collecting information about particular themes, they usually employ various mediums such as social networking services, Internet news, and blogs. However, in terms of management, each document circulated through diverse mediums is placed in different categories on the basis of each source's policy and standards, hindering any attempt to conduct research on a specific category across different kinds of sources. For example, documents containing content on "Application for a foreign travel" can be classified into "Information Technology," "Travel," or "Life and Culture" according to the peculiar standard of each source. Likewise, with different viewpoints of definition and levels of specification for each source, similar categories can be named and structured differently in accordance with each source. To overcome these limitations, this study proposes a plan for conducting category mapping between different sources with various mediums while maintaining the existing category system of the medium as it is. Specifically, by re-classifying individual documents from the viewpoint of diverse sources and storing the result of such a classification as extra attributes, this study proposes a logical layer by which users can search for a specific document from multiple heterogeneous sources with different category names as if they belong to the same source. Besides, by collecting 6,000 articles of news from two Internet news portals, experiments were conducted to compare accuracy among sources, supervised learning and semi-supervised learning, and homogeneous and heterogeneous learning data. It is particularly interesting that in some categories, classifying accuracy of semi-supervised learning using heterogeneous learning data proved to be higher than that of supervised learning and semi-supervised learning, which used homogeneous learning data. This study has the following significances. First, it proposes a logical plan for establishing a system to integrate and manage all the heterogeneous mediums in different classifying systems while maintaining the existing physical classifying system as it is. This study's results particularly exhibit very different classifying accuracies in accordance with the heterogeneity of learning data; this is expected to spur further studies for enhancing the performance of the proposed methodology through the analysis of characteristics by category. In addition, with an increasing demand for search, collection, and analysis of documents from diverse mediums, the scope of the Internet search is not restricted to one medium. However, since each medium has a different categorical structure and name, it is actually very difficult to search for a specific category insofar as encompassing heterogeneous mediums. The proposed methodology is also significant for presenting a plan that enquires into all the documents regarding the standards of the relevant sites' categorical classification when the users select the desired site, while maintaining the existing site's characteristics and structure as it is. This study's proposed methodology needs to be further complemented in the following aspects. First, though only an indirect comparison and evaluation was made on the performance of this proposed methodology, future studies would need to conduct more direct tests on its accuracy. That is, after re-classifying documents of the object source on the basis of the categorical system of the existing source, the extent to which the classification was accurate needs to be verified through evaluation by actual users. In addition, the accuracy in classification needs to be increased by making the methodology more sophisticated. Furthermore, an understanding is required that the characteristics of some categories that showed a rather higher classifying accuracy of heterogeneous semi-supervised learning than that of supervised learning might assist in obtaining heterogeneous documents from diverse mediums and seeking plans that enhance the accuracy of document classification through its usage.

• The Journal of Korean Philosophical History
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• no.23
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• pp.7-65
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• 2008

The object of this essay is to apply the Leadership Theory, current interest in Asian Philosophy, to Korean appliance. This is to associate contemporary Leadership Theory with Chosun Confucianism in order to discover the Korean Leadership Prototype, and seek the possibility of applying it for modern usage. The essay uses two analysis models. The tools used for the methodology consists of the personal characteristics of the leader as one axis and ruling out the roles in order to develop the discussion as the other axis. First axis is the process of the leader setting the identity and strengthening the ability to successfully deploy his/her leadership. The second axis is comprised of four specific fields where the leadership is deployed. The four sectors are Self Sector, Relationship Sector, Team Sector and Community Sector. Core values of each sector have been set up and specific competences have been presented. In the Self Sector, $zh{\grave{i}}x{\bar{i}}n$(治心) and $ch{\acute{i}}j{\grave{i}}ng$(持敬) have been set as core values and $l{\grave{i}}zh{\grave{i}}$(立志) and $sh{\acute{i}}x{\bar{i}}n$(實心) as their competences. In the Relationship Sector and Team Sector, circumstances(時宜) and $sh{\acute{i}}sh{\grave{i}}g{\bar{e}}ngzh{\bar{a}}ng$(實事更張) were set as core values, accordingly. Lastly for the Community sector, the core value, 'Ideal Leader and the Visions of and Ideal Community', was conceptualized as '$m{\grave{ui}}m{\acute{i}}nzh{\grave{i}}zh{\grave{i}}$(牧民至治)'. The leadership is then expanded from the Self Sector to the final stage through its processes. Through this research, it can be found out that the Korean Leadership Model is not rigid to just cover a specific point in time or situation, but embraces many contemporary leadership concepts, thus having the characteristics of a comprehensive leadership theory.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.