• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.119-129
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• 2018

The Ministry of Land, Transport and Maritime Affairs has promulgated the mandatory design of BIM for road projects of more than 50 billion won by 2020 under the Basic Plan for the Sixth Construction Technology Promotion. As a result, major public clients are attempting to implement BIMs that are appropriate to the situation of each institution. On the other hand, it is difficult to design and construct a proper BIM and accumulate BIM information of the ordering organization because the technical guidelines and standard classification system that can perform BIM effectively have not been presented sufficiently. The characteristics of the road should be managed systematically, e.g., atypical objects, such as earthworks, which are constantly changing along a line; large objects, such as bridges and tunnels; and facilities, such as signs and soundproof walls. To achieve this, a multitude of standard systems should be developed and disseminated, but there have been insufficient studies on practical methods. To solve this problem, this study developed a BIM standard object classification system in the road sector to meet the international standard, accommodate a multi-dimensional information system, and provide a more effective BIM standard information environment that can be utilized easily by practitioners.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.3
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• pp.211-221
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• 2009

With recent trends of super-tallness, atypical plan shapes and zoning constructions in high-rise buildings, a structural stability of the building under construction is arising as a key issue for design and construction plan. To ensure the structural stability under construction, the differential column shortening of vertical members, the lateral displacement of tower frames, and differential settlement of raft foundation by unbalanced distributions of a tower self-weight before the completion of a lateral load resisting system should be checked by construction sequence analysis, which should be performed by systematic combinations with structural health monitoring, construction compensation program, and construction panning. This paper presents the scheme of zone-based construction sequence analysis by using the existing commercial analysis program, to check the stability of high-rise building under construction. This scheme is applied to 3-dimensional structural analysis for a real high-rise building under construction. The analysis includes real construction zoning plans and schedules as well as creep and shrinkage effects and time-dependent properties of concrete. The simplified construction sequence and assumed material properties were continuously updated with the change on construction schedule and correlations with in-situ measurement data.

• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.33-47
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• 2010

The growth of IT technology and the popularity of network based information sharing increase the number of digital contents in military area. Thus, there arise issues of finding suitable public information with the growing number of long-term preservation of digital public information. According to the source of raw data and the time of compilation may be variable and there can be existed in many correlations about digital contents. The business service ontology makes knowledge explicit and allows for knowledge sharing among information provider and information consumer for public digital archive engaged in improving the searching ability of digital public information. The business service ontology is at the interface as a bridge between information provider and information consumer. However, according to the difficulty of semantic knowledge extraction for the business process analysis, it is hard to realize the automation of constructing business service ontology for mapping from unformed activities to a unit of business service. To solve the problem, we propose a new business service auto-acquisition method for the first step of constructing a business service ontology based on Enterprise Architecture.

• Design Convergence Study
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• v.16 no.6
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• pp.169-184
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• 2017

There is a lot of needs that are not expressed as much as the expressed needs in familiar products and services that are used in daily life such as a smartphone. Finding the 'Inconveniences in familiar use' make it possible to create opportunities for value expanding in the existing products and service area. There are a lot of related works, which have studied the definition of hidden needs and the methods to find it. But, they are making it difficult to address the hidden needs in the cases of familiar use due to focus on the new product or service developing typically. In this study, we try to redefine the hidden needs in the daily familiarity and approach it in the new way to find out. Because of the users' unability to express what they want and the complexity of needs which can not be explained clearly, we can not approach it as the quantitative issue. For this reason, the basic data type selected as the user behavior data excluding all description is the screen-shot of the smartphone. We try to apply the integrated rules and patterns to the individual data using the qualitative coding techniques to overcome the limitations of qualitative analysis based on unstructured data. From this process, We can not only extract meaningful clues which can make to understand the hidden needs but also identify the possibility as a way to discover hidden needs through the review of relevance to actual market trends. The process of finding hidden needs is not easy to systemize in itself, but we expect the possibility to be conducted a reference frame for finding hidden needs of other further studies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.1
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• pp.43-52
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• 2016

Due to the characteristics of microwave signals, Radar satellite image has been used for flood detection without weather and time influence. The more methods of flood detection were developed, the more detection rate of flood area has been increased. Since flood causes a lot of damages, flooded area should be distinguished from non flooded area. Also, the detection of flood area should be accurate. Therefore, not only image resolution but also the filtering process is critical to minimize resolution degradation. Although a resolution of radar images become better as technology develops, there were a limited focused on a highly suitable filtering methods for flood detection. Thus, the purpose of this study is to find out the most appropriate filtering method for flood detection by comparing three filtering methods: Lee filter, Frost filter and NL-means filter. Therefore, to compare the filters to detect floods, each filters are applied to the radar image. Comparison was drawn among filtered images. Then, the flood map, results of filtered images are compared in that order. As a result, Frost and NL-means filter are more effective in removing the speckle noise compared to Lee filter. In case of Frost filter, resolution degradation occurred severly during removal of the noise. In case of NL-means filter, shadow effect which could be one of the main reasons that causes false detection were not eliminated comparing to other filters. Nevertheless, result of NL-means filter shows the best detection rate because the number of shadow pixels is relatively low in entire image. Kappa coefficient is scored 0.81 for NL-means filtered image and 0.55, 0.64 and 0.74 follows for non filtered image, Lee filtered image and Frost filtered image respectively. Also, in the process of NL-means filter, speckle noise could be removed without resolution degradation. Accordingly, flooded area could be distinguished effectively from other area in NL-means filtered image.

• Knowledge Management Research
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• v.21 no.3
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• pp.27-44
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• 2020

In order to solve the user's information overload problem, recommender systems infer users' preferences and suggest items that match them. The collaborative filtering (CF), the most successful recommendation algorithm, has been improving performance until recently and applied to various business domains. Visual information, such as book covers, could influence consumers' purchase decision making. However, CF-based recommender systems have rarely considered for visual information. In this study, we propose VizNCS, a CF-based deep learning model that uses visual information as additional information. VizNCS consists of two phases. In the first phase, we build convolutional neural networks (CNN) to extract visual features from image data. In the second phase, we supply the visual features to the NCF model that is known to easy to extend to other information among the deep learning-based recommendation systems. As the results of the performance comparison experiments, VizNCS showed higher performance than the vanilla NCF. We also conducted an additional experiment to see if the visual information affects differently depending on the product category. The result enables us to identify which categories were affected and which were not. We expect VizNCS to improve the recommender system performance and expand the recommender system's data source to visual information.

• Journal of Life Science
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• v.33 no.12
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• pp.1036-1045
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• 2023

Sarcopenia, a condition characterized by the insidious loss of skeletal muscle mass and strength, represents a significant and growing healthcare challenge, impacting the mobility and quality of life of aging populations worldwide. This study investigated the therapeutic potential of soybean leaf extract (SL) for dexamethasone (Dexa)-induced muscle atrophy in vitro and in an in vivo model. In vitro experiments showed that SL significantly alleviated Dexa-induced atrophy in C2C12 myotube cells, as evidenced by preserved myotube morphology, density, and size. Moreover, SL treatment significantly reduced the mRNA and protein levels of muscle RING-finger protein-1 (MuRF1) and muscle atrophy F-box (MAFbx), key factors regulating muscle atrophy. In a Dexa-induced atrophy mouse model, SL administration significantly inhibited Dexa-induced weight loss and muscle wasting, preserving the mass of the gastrocnemius and tibialis anterior muscles. Furthermore, mice treated with SL exhibited significant improvements in muscle function compared to their counterparts suffering from Dexa-induced muscle atrophy, as evidenced by a notable increase in grip strength and extended endurance on treadmill tests. Moreover, SL suppressed the expression of muscle atrophy-related proteins in skeletal muscle, highlighting its protective role against Dexa-induced muscle atrophy. These results suggest that SL has potential as a natural treatment for muscle-wasting conditions, such as sarcopenia.

• Journal of Korean Orthopaedic Sports Medicine
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• v.10 no.1
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• pp.27-34
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• 2011

Purpose: To evaluate the short term clinical result of ACL reconstruction with autologous hamstring tendon using Cross pin and Graft Tensioner and to seek way to resolve the experienced technical problems. Materials and Methods: From January 2008 to March 2009, 35 patients who had been treated arthroscopic ACL reconstruction were enrolled. The femoral side of ACL graft was fixed with Cross pin. The tibial side of graft was fixed with Intrafix and post-tie. The graft was tensioned with Graft Tensioner at 80N. We analyzed the clinical, radiographic results and complications. Results: IKDC subjective score and Lysholm score improved to 89.1 and 91.4 points. Also, Pivot shift test and One-leg hop test showed good results postoperatively. Side to side difference using KT-1000 arthrometer and Telos stress radiography improved compared with normal limb to $2.8{\pm}1.6$ mm and $2.6{\pm}1.3$ mm, respectively. The femoral tunnel enlarged to $2.3{\pm}1.1$ mm. Soft tissue irritation and femoral tunnel-graft harness length mismatch, femoral tunnel-cross pin tunnel mismatch were happened as peri-operative complications. Conclusion: Using of Cross pin and Graft Tensioner for ACL reconstruction with hamstring tendon is one of the good method for obtaining stability in short-term clinical result. But to reduce femoral tunnel-cross pin mismatch, it needs to shorten femoral bone tunnel and to create cross pin tunnel as vertical as possible. And to reduce femoral tunnel-graft harness mismatch, it needs to advance position rod further 3 mm when to create femoral tunnel.