• Journal of Trauma and Injury
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• v.27 no.4
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• pp.151-157
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• 2014

Purpose: We report the surgical outcomes for femoral shaft fractures in polytrauma patients who were temporarily treated with external fixation and subsequently converted to internal fixation. Methods: From August 2008 to April 2012, we enrolled 13 patients with multiple traumas due to high-energy injuries and concurrent femoral shaft fractures in which temporary external fixation was carried out. The mean age was 39 years, with a range from 18 to 55 years. Ten were men and 3 were women. According to the AO/OTA classification of fractures, type A was found in 5 patients, type B in 6, and type C in 2, with open fractures being found in 6 patients and femoral artery rupture occurring in 2. For internal fixation, intramedullary nailing was performed in 7 patients, and minimally-invasive fixation of locking compression plates was used in 6. Results: Of the 7 patients converted to intramedullary nailing, 1 experienced delayed union. Of the 6 patients treated with minimally-invasive plate fixation, delayed union occurred in 5, and an auto-bone graft was performed within, on average, 8 months (range: 5~10 months), leading to bone union in all cases in the final follow-up. None of the patients experienced infections or complications involving other organs after having been converted to internal fixation. During the mean follow-up of 19 months, patients achieved satisfactory functional outcomes. Conclusion: In polytrauma patients with a femoral shaft fracture who have been treated with temporary external fixation and who may need internal fixation due to the occurrence of delayed union, an appropriate internal fixation method needs to be selected based on the patient's physical status, and the fracture type.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.341-346
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• 2012

Social network service is service that enables the human network to be built up on web. It is important to record users' information simply and establish the network with people based on the information to provide with the social network service effectively. But it is very troublesome work for the user to input his or her own information on the mobile environment. In this paper we suggested a system which classifies users' behavior using context and creates blogging sentences automatically after inferring the destination. For this, users' behavior is classified and the destination is inferred with the sequence matching method using Naive Bayes classification. Then sentences which are suitable for situation is created by arranging the processed context using the structure of 5W1H. The system was evaluated satisfaction degree by comparing the created sentences based on actually collected data with users' intension and got accuracy rate of 88.73%.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.4
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• pp.171-185
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• 2014

The purposes of this study are to examine the various methods of subject access in the rapidly changing environment and to suggest the future of subject access in National Library of Korea (NLK). First of all, current status and problems of Library of Congress Subject Headings List as an representative subject headings in the world and the ways of improving effectiveness of subject retrieval were dealt with. As the ways of improving subject access, social bookmarking, folksonomy, tagging, facet applications, automatic assignment of keyword, thesauri, classification system, and auto-assigned search box were suggested. Finally, current status of NLK subject headings and the ways of improving for utilization of the subject headings as subject access were provided.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.656-662
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• 2012

In this paper, we describe a new summarizing method based on a graph-based and a sense-based analysis. In the graph-based analysis, we convert sentences in a document into word vectors and calculate the similarity between each sentence using LSA. We reflect this similarity of sentences and the rarity scores of words in sentences to define weights of edges in the graph. Meanwhile, in the sense-based analysis, in order to determine the sense of words, subjectivity or objectivity, we built a database which is extended from the golden standards using Wordnet. We calculate the subjectivity of sentences from the sense of words, and select more subjective sentences. Lastly, we combine the results of these two methods. We evaluate the performance of the proposed method using classification games, which are usually used to measure the performances of summarization methods. We compare our method with the MS-Word auto-summarization, and verify the effectiveness of ours.

• Journal of Fashion Business
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• v.16 no.6
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• pp.21-35
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• 2012

In consideration of the injuries and deaths occurring at manufacturing sites due to the use of inappropriate work clothes or safety devices, this study aims to categorize manufacturing work processes to develop functional work clothes for heavy industries including the automobile, machine and shipbuilding industries in South Korea. Defining the features of the work environments and work postures of these industries provided for a categorization of the work processes which would enable the development of suitable work clothes for each work process' category. The results of the study based on a questionnaire survey are as follows: Work process category 1, including steel panel pressing and auto body assembly, final inspection (in automobile) and inspection (in machine), requires work clothes with upper body and arm mobility and performance to protect from the toxic fume factor. Work process category 2, consisting of welding (in automobile), cutting-and-forming (in machine) and attachment-and-construction (in shipbuilding), requires clothing elasticity, durability and heat and fire resistance. Work process category 3 comprising welding and grinding in the machine and shipbuilding industries, requires work clothes' tear resistance and elasticity, particularly for lateral bending mobility, and work clothes' sleeves' and pants' hemlines with sealed designs to defend against iron filing penetration, as well as incombustible and heat-resistant material performance. Finally, work process category 4, including painting in machine and shipbuilding, requires work clothes with waterproofing, air permeability, thermal performance, elasticity, durability and abrasion resistance.

• International Journal of Highway Engineering
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• v.11 no.1
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• pp.59-67
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• 2009

With the development of high-cost vehicle detectors, low-cost detectors have also been studied due to the advantage that more detectors are provided within limited budgets. This study proposed a method to estimate vehicle speeds using vehicles' track data from auto manufacturers and time stamps obtained when vehicles' tires pass an inclined sensor (here, a tape switch sensor). In speed estimation, small vehicles and large vehicles is distinguished according to the ratio of time stamps for a wheelbase and a rear track obtained from a tape switch sensor. In particular, speed estimation can be adjusted through a parameter to determine vehicles' size so as to take into account location properties such as vehicles' classification ratio. The low-cost vehicle detector with an inclined sensor proposed in this study is expected to be widely utilized to monitor traffic conditions thanks to low cost.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• Journal of Information Technology Services
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• v.22 no.2
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• pp.1-21
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• 2023

Due to the convergence and complexity of the 4th Industrial Revolution, the boundaries between industries have become unclear and ambiguous. Consequently, there is a lack of research on how firms engaged in this industry are changing their location behavior. Recently, some attempts to classify the industrial groups of the 4th Industrial Revolution and their detail occupations have been made, and this study adopts the classification of Lee and Jung (2020) of the Korea Institute for Industrial Economics & Trade. In this study, the 18 detailed industries commonly included in multiple industrial groups are defined as 'core industries' and are classified into manufacturing and service industries to explore the spatial patterns of firms' location. Specifically, this study aims to examine how the location behavior of firms in core industries of the 4th Industrial Revolution has changed from 2010 to 2019 in the Seoul metropolitan area, using the 「National Business Survey」 data. We employed two methods based on spatial auto-correlation: (i) spatial kernel density estimation analysis and (ii) local Moran's I_i analysis. The results indicate that the core industry firms form more distinct and larger clusters in 2019 based on the clusters formed in 2010. Specifically, manufacturing industry firms tended to concentrate in the southern region of Gyeonggi and parts of Seoul, while serivce industry firms were more concentrated in Seoul. These core industries play a critical role in industries and are closely related to the ICT industries, which generate high-added value and increase productivity in the front and rear industries. This study reveals that the agglomeration of these industries in specific regions is intensifying and may exacerbate regional inequality.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Smart Media Journal
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• v.12 no.11
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• pp.1-9
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• 2023

Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.