• Journal of Digital Convergence
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• v.11 no.3
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• pp.363-369
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• 2013

In this paper, the modeling of phenomena that occurs in the brain related language was designed and developed the second language proficiency method. This method based on cognitive ability test in cognitive psychology that is the lexical decision task, the priming task and the verbal span task. The lexical decision task involves measuring how quickly decide stimuli as words or nonwords. This task is divided reading and listening according to stimulus type to the details. The priming task finds the output of the language. This task is divided the translation-priming and the semantic-priming according to stimulus type. The verbal span task finds the short term memory. In this paper, we propose the second language proficiency measurement method using the linguistics cognitive ability of the learner about the second language.

• Journal of KIISE
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• v.43 no.4
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• pp.470-479
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• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.

• Archives of design research
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• v.17 no.1
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• pp.201-210
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• 2004

The web as a sign synthesis text has become a kernel for incorporeal knowledge as well as a communication model through the ubiquitous environment all over the world. The evaluation of the communication model, which is essential for the information structure, acts as an important basis on determining the quality of the web contents. In this study, the development of the progress of the communication of semantic meaning in the construction of the information structure was analyzed in views of the form, the function, and the emotional effect of the rich media contents of the web. The transformation process from the initial access elements through the final selection elements was suggested as the communication model and the effects of the function of the information in the web on the process was assessed by the engineering and linguistic models of Shannon, Weaver, and Roman Jakobson. The results of this study showed that the environments such as the speed, the memory space, data compression technique, and data filtering have influences on the web contents expression and the evaluation of the communication model in connection with the environments is the basis in the information structure.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.197-202
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• 2010

Recent studies on change detection for RDF data are focused on not only the structural difference but also the semantic-aware difference by computing the closure of RDF models. However, since these techniques which take into account the semantics of RDF model require both RDF models to be memory resident, or they use a forward-chaining strategy which computes the entire closure in advance, it is not efficient to apply them directly to detect changes in large RDF data. In this paper, we propose a scalable change detection technique for RDF data, which uses a backward-chaining inference based on relational database. Proposed method uses a new approach for RDF reasoning that computes only the relevant part of the closure for change detection in a relational database. We show that our method clearly outperforms the previous works through experiment using the real RDF from the bioinformatics domain.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.608-620
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• 2021

Recent deep learning-based face super-resolution (FSR) works showed significant performances by utilizing facial prior knowledge such as facial landmark and dictionary that reflects structural or semantic characteristics of the human face. However, most of these methods require additional processing time and memory. To solve this issue, this paper propose an efficient FSR models using knowledge distillation techniques. The intermediate features of teacher network which contains dictionary information based on major face regions are transferred to the student through adversarial multi-scale features distillation. Experimental results show that the proposed model is superior to other SR methods, and its effectiveness compare to teacher model.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.2
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• pp.95-115
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• 2023

This study designed a data model linked to Wikidata and examined its applicability to increase the utilization of the digital archive records of the National Debt Redemption Movement, registered as World Memory Heritage, and implications were derived by analyzing the existing metadata, thesaurus, and semantic network graph. Through analysis of the original text of the National Debt Redemption Movement records, key data model classes for linking with Wikidata, such as record item, agent, time, place, and event, were derived. In addition, by identifying core properties for linking between classes and applying the designed data model to actual records, the possibility of acquiring abundant related information was confirmed through movement between classes centered on properties. Thus, this study's result showed that Wikidata's strengths could be utilized to increase data usage in local archives where the scale and management of data are relatively small. Therefore, it can be considered for application in a small-scale archive similar to the National Debt Redemption Movement digital archive.

• International Journal of Advanced Culture Technology
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• v.5 no.3
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• pp.11-23
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• 2017

Anatomy is one of the basic subjects of the nursing course, which is included in the curriculum of the nursing. Anatomy is a basic course for understanding major in nursing and it is the first gateway to acquire expertise. It is mainly opened in nursing and first to second grades. Therefore, students who have advanced to the nursing department have great interest and expectation on the anatomy subjects. In general, nursing students are studying anatomy with theories and models, and some universities practice on tour after dissection of medical consortium for short time. This is called the Cadaver practice. This study was carried out to investigate the thoughts and experiences of bioethics through nursing students' Cadaver practice. The interview data were processed through the analysis and interpretation process using the phenomenological research method, Giorgi method. As a result, 48 semantic units were derived, and then they were divided into 10 subcomponents and divided into 6 categories. As a result of the analysis, the experience of nursing students' bioethics was tied to the topic of six questions. For example, 'what about the respect and responsibility of nursing students in Cadaver?', 'What about your experience with the Cadaver experiment and bioethics?', 'What was the academic achievement of the actual human body structure viewing experience with cadaver?', 'What was the connection with theory?', 'What was your intention to recommend to others?', 'What was your perception and interest in the Cadaver experiment?'. Analyzes were integrated into 10 structures; "Thank you for your donation", "want others to refuse donation", "Burden of practice", "Good opportunities for learning", "Understand better", "Should study harder", "Memory is better", "Compared to theory", "Good experience", "Want to rejoin". The general structural description of the participants' meanings is summarized as follows. Nursing college students who participated in the Cadaver had a gratitude for the donor, but they said they would like to talk to others about donating organs. Before they went to practice, they felt a lot of pressure on the dissection of the cadaver, but they went to the practice and thought that it was a good opportunity to learn by doing well. Specifically, they understood that they had better understanding than the theoretical lesson, and that they were more eager to study their major through practice. In other words, most of them were more memorable and they would like to participate again if the opportunity comes next time. The results of this study show that the practice of Cadaver in nursing college students is very positive in terms of educational effectiveness. However, in terms of bioethics, it can be seen that the education process is somewhat unsatisfactory. Therefore, the systematic bioethics education should be prepared before the practice in the nursing college students' Cadaver practice.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.4
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• pp.1-10
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• 2010

The scientific verification of the physiological and psychological effects that result from interaction with green plants would not only provide objective knowledge on the psychological effect of green but would also establish useful grounds for the creation of green spaces that consider human emotions. The present study measured the cerebral activity(cerebral blood flow) and the autonomic nervous system activity (blood pressure, pulse rate, amylase) of fifteen Korean male subjects as they inhaled the natural scent diffused by pine needles. Impression and mood state evaluations of the reactions to the pine scent were carried out using the SD method and POMS. Cerebral activity was observed to be significantly activated in the feeling, judgment, and motor areas of the frontal lobe, as well as the memory area in the temporal lobe. Verbal evaluations by the SD method and POMS indicated a pine scent left natural but stimulated and active impressions, provided vigor, and also reduced confusion. The autonomic nervous system activities, however, showed no significant differences. These findings verified scientifically that a pine scent vitalizes humans both physiologically and psychologically. These results could be useful as fundamental data for the design of green spaces that consider human emotional aspects.

• The KIPS Transactions:PartA
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• v.18A no.5
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• pp.193-204
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• 2011

Since web applications are accessed by anonymous users via web, more security risks are imposed on those applications. In particular, because security vulnerabilities caused by insecure source codes cannot be properly handled by the system-level security system such as the intrusion detection system, it is necessary to eliminate such problems in advance. In this paper, to enhance the security of web applications, we develop a static analyzer for detecting the well-known security vulnerability of PHP file inclusion vulnerability. Using a semantic based static analysis, our vulnerability analyzer guarantees the soundness of the vulnerability detection and imposes no runtime overhead, differently from the other approaches such as the penetration test method and the application firewall method. For this end, our analyzer adopts abstract interpretation framework and uses an abstract analysis domain designed for the detection of the target vulnerability in PHP programs. Thus, our analyzer can efficiently analyze complicated data-flow relations in PHP programs caused by extensive usage of string data. The analysis results can be browsed using a JAVA GUI tool and the memory states and variable values at vulnerable program points can also be checked. To show the correctness and practicability of our analyzer, we analyzed the source codes of open PHP applications using the analyzer. Our experimental results show that our analyzer has practical performance in analysis capability and execution time.