• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1844-1866
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• 2011

In a home network system, each customer electronic device has the control data format chosen by its manufacturing company and there are various types of digital devices and protocols. Besides the mutual operating environments among the various devices are dissimilar. Affected by the characteristics explained above, home network systems can hardly support the crucial functions, such as data compatibility, concurrency control, and dynamic plug-in. Thus, the home network system shows relatively poor reusability. In this paper, we suggest design technique of configurable framework, which can widely support the variability, to increase the reusability of the home network system. We extract the different parts of the home network system as variation points, and define them as the variability types. We design a structure of configurable framework, and suggest customization technique of configurable framework through selection technique and plug-in technique. Also, we prove the reusability by applying the proposed framework and it methods to real-world home network systems and analyzing the measurement results of these case studies using software metrics. We can expect the proposed approach provides better reusability than the existing them by analyzing those measurement results.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.69-79
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• 2008

Due to popularization of the wireless Internet and mobile devices the infrastructure of the ubiquitous environment, where users can get information whatever they want anytime and anywhere, is created. Therefore, a variety of fields including the education studies methods for efficiency of information transmission using on-line and off-line contents. In this paper, we propose the Mixed Mobile Education system(MME) that improves educational efficiency using on-line and off-line contents on mobile devices. Because it is hard to input new data and cannot use similar off-line contents in systems used additional tags, the proposed system does not use additional tags but recognizes of-line contents as we extract feature points in the input image using the mobile camera. We use the Scale Invariant Feature Transform(SIFT) algorithm to extract feature points which are not affected by noise, color distortion, size and rotation in the input image captured by the low resolution camera. And we use the client-server architecture for solving the limited storage size of the mobile devices and for easily registration and modification of data. Experimental results show that compared with previous work, the proposed system has some advantages and disadvantages and that the proposed system has good efficiency on various environments.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• 한국벤처창업학회:학술대회논문집
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• 2008.04a
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• pp.179-208
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• 2008

In this research, an empirical analysis was performed to determine the correlation between management performance and R&D investment for domestic venture businesses in each industry. Specifically, an empirical analysis for each industry was attempted not only to clarify the general hypothesis on the relationship between management performance and R&D investment for venture businesses but also to demonstrate that differences exist for each industry. Empirical analysis was conducted for eight industries with respect to the $2002{\sim}2006$ panel data extracted as investigative results from the "Investigation Report on Science and Technology R&D Activities" published by the Ministry of Science and Technology. Industrial classification was limited to the middle-level classification (2-digit) in the Korea Standard Industry Code (KSIC) owing to the limited number of panels. Although this research only verified the overall positive effect of R&D activities and funds for existing research on corporate value or productivity and management performance, it was able to document the difference for each individual industry and each business size unlike existing research.Furthermore, the reliability of the research results was enhanced by targeting companies that have been continuously conducting R&D and management activities using consistent 5-year panel data in the analysis. Again, this was something that existing research did not have. Finally, through the use of recent data from 2002 after the IMF economic crisis up to 2006 in the empirical analysis, this research proposed the problems due to the prevailing circumstances at the time of entering the advanced nation stage based on an empirical analysis; the prevailing problems during the pursuit of advanced nation status before the IMF crisis broke out were not tackled. The key empirical analysis yielded several results. First, capital and size of the labor force have a positive correlation with the management performance for the entire company or the venture business. This applies to all eight industries as the subjects of the analysis. Second, although the number of years since a company has been established can have positive or negative correlation with management performance for the entire company or venture business in specific industries, a definite overall trend cannot be identified. Third, R&D investment can be said to have an overall positive effect on corporate management performance. Fourth, the size of the research staff cannot be said to be a factor unilaterally affecting the management performance of the entire company or the venture business. Fifth, the number of years a research institute has been in operation, which was assumed to have a positive effect on the management performance of a company because of the accumulated R&D know-how -- definitely acts as a positive factor contributing to the management performance of a company.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.81-96
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• 2013

Video information retrieval has become a very important issue because of the explosive increase in video data from Web content development. Meanwhile, content-based video analysis using visual features has been the main source for video information retrieval and browsing. Content in video can be represented with content-based analysis techniques, which can extract various features from audio-visual data such as frames, shots, colors, texture, or shape. Moreover, similarity between videos can be measured through content-based analysis. However, a movie that is one of typical types of video data is organized by story as well as audio-visual data. This causes a semantic gap between significant information recognized by people and information resulting from content-based analysis, when content-based video analysis using only audio-visual data of low level is applied to information retrieval of movie. The reason for this semantic gap is that the story line for a movie is high level information, with relationships in the content that changes as the movie progresses. Information retrieval related to the story line of a movie cannot be executed by only content-based analysis techniques. A formal model is needed, which can determine relationships among movie contents, or track meaning changes, in order to accurately retrieve the story information. Recently, story-based video analysis techniques have emerged using a social network concept for story information retrieval. These approaches represent a story by using the relationships between characters in a movie, but these approaches have problems. First, they do not express dynamic changes in relationships between characters according to story development. Second, they miss profound information, such as emotions indicating the identities and psychological states of the characters. Emotion is essential to understanding a character's motivation, conflict, and resolution. Third, they do not take account of events and background that contribute to the story. As a result, this paper reviews the importance and weaknesses of previous video analysis methods ranging from content-based approaches to story analysis based on social network. Also, we suggest necessary elements, such as character, background, and events, based on narrative structures introduced in the literature. We extract characters' emotional words from the script of the movie Pretty Woman by using the hierarchical attribute of WordNet, which is an extensive English thesaurus. WordNet offers relationships between words (e.g., synonyms, hypernyms, hyponyms, antonyms). We present a method to visualize the emotional pattern of a character over time. Second, a character's inner nature must be predetermined in order to model a character arc that can depict the character's growth and development. To this end, we analyze the amount of the character's dialogue in the script and track the character's inner nature using social network concepts, such as in-degree (incoming links) and out-degree (outgoing links). Additionally, we propose a method that can track a character's inner nature by tracing indices such as degree, in-degree, and out-degree of the character network in a movie through its progression. Finally, the spatial background where characters meet and where events take place is an important element in the story. We take advantage of the movie script to extracting significant spatial background and suggest a scene map describing spatial arrangements and distances in the movie. Important places where main characters first meet or where they stay during long periods of time can be extracted through this scene map. In view of the aforementioned three elements (character, event, background), we extract a variety of information related to the story and evaluate the performance of the proposed method. We can track story information extracted over time and detect a change in the character's emotion or inner nature, spatial movement, and conflicts and resolutions in the story.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.3 no.1
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• pp.1-28
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• 2008

In this research, an empirical analysis was performed to determine the correlation between management performance and Empirical Research on the R&D investment for domestic venture businesses in each industry. Specifically, an empirical analysis for each industry was attempted not only to clarify the general hypothesis on the relationship between management performance and R&D investment for venture businesses but also to demonstrate that differences exist for each industry. Empirical analysis was conducted for eight industries with respect to the $2002{\sim}2006$ panel data extracted as investigative results from the "Investigation Report on Science and Technology R&D Activities" published by the Ministry of Science and Technology. Industrial classification was limited to the middle-level classification (2-digit) in the Korea Standard Industry Code (KSIC) owing to the limited number of panels. Although this research only verified the overall positive effect of R&D activities and funds for existing research on corporate value or productivity and management performance, it was able to document the difference for each individual industry and each business size unlike existing research. Furthermore, the reliability of the research results was enhanced by targeting companies that have been continuously conducting R&D and management activities using consistent 5-year panel data in the analysis. Again, this was something that existing research did not have. Finally, through the use of recent data from 2002 after the IMF economic crisis up to 2006 in the empirical analysis, this research proposed the problems due to the prevailing circumstances at the time of entering the advanced nation stage based on an empirical analysis; the prevailing problems during the pursuit of advanced nation status before the IMF crisis broke out were not tackled. The key empirical analysis yielded several results. First, capital and size of the labor force have a positive correlation with the management performance for the entire company or the venture business. This applies to all eight industries as the subjects of the analysis. Second, although the number of years since a company has been established can have positive or negative correlation with management performance for the entire company or venture business in specific industries, a definite overall trend cannot be identified. Third, R&D investment can be said to have an overall positive effect on corporate management performance. Fourth, the size of the research staff cannot be said to be a factor unilaterally affecting the management performance of the entire company or the venture business. Fifth, the number of years a research institute has been in operation, which was assumed to have a positive effect on the management performance of a company because of the accumulated R&D know-how -- definitely acts as a positive factor contributing to the management performance of a company.