• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.512-522
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• 2002

In this paper, we propose a hybrid method which is mixed with frame selection and weighting model rank method, based on GMM(gaussian mixture model), for real-time text-independent speaker recognition system. In the system, maximum likelihood estimation was used for GMM parameter optimization, and maximum likelihood was used for recognition basically Proposed hybrid method has two steps. First, likelihood score was calculated with speaker models and test data at frame level, and the difference is calculated between the biggest likelihood value and second. And then, the frame is selected if the difference is bigger than threshold. The second, instead of calculated likelihood, weighting value is used for calculating total score at each selected frame. Cepstrum coefficient and regressive coefficient were used as feature parameters, and the database for test and training consists of several data which are collected at different time, and data for experience are selected randomly In experiments, we applied each method to baseline system, and tested. In speaker recognition experiments, proposed hybrid method has an average of 4% higher recognition accuracy than frame selection method and 1% higher than W method, implying the effectiveness of it.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1204-1212
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• 2008

The vehicle simulation (flight simulator) in 1920's was the first in the virtual reality. With the development of precise optical and electromagnetic equipment, the virtual reality widened its application for other purposes than military one. Based on the realistic display technology, it is more and more common in the various areas such as entertainment, medical profession, learning, film, architectural design, tourism and etc. In 1989, Jaron Ranier was the first to use the terminology 'Virtual Reality'. With this term, all virtual projects could be classified in a single item. But even before the term was used, the virtual reality has been studied up to now. As a part of virtual reality, the human thirst for the impossible thing has led to the development of ride film. The ride film consists of the special technical elements as well as the psychological analysis of human being. The ultimate purpose of virtual reality is engrossment through interaction. Even though the real interaction requires interface, input sensor and reaction ability, the ride film is not an element of the typical interaction. The virtual reality is mostly defined in technical terms now. But in this study, we will analyze the concepts worked out by Professor Michael Haim who is called a philosopher in the cyberspace in aspect of experience-oriented definition. We will analyze the adaptability of virtual reality based on his concepts such as artificial reality/ interaction/ engrossment/ networked world/ remote display/ simulation/ onmon engrossment. And also, we aim to suggest the directions of developing the ride films for perfect engrossment and to draw optimized conclusion thereon. In this viewpoint, we consider that the study of ride film on which there were few case studies will contribute to level up the basic frameworks of IT technology and the digital image.

• Smart Media Journal
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• v.11 no.5
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• pp.17-25
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• 2022

Korean part-of-speech taggers decompose a compound morpheme into unit morphemes and attach part-of-speech tags. So, here is a disadvantage that part-of-speech for morphemes are over-classified in detail and complex word types are generated depending on the purpose of the taggers. When using the part-of-speech tagger for keyword extraction in deep learning based language processing, it is not required to decompose compound particles and verb-endings. In this study, the part-of-speech tagging problem is simplified by using a Head-Tail tokenization technique that divides only two types of tokens, a lexical morpheme part and a grammatical morpheme part that the problem of excessively decomposed morpheme was solved. Part-of-speech tagging was attempted with a statistical technique and a deep learning model on the Head-Tail tokenized corpus, and the accuracy of each model was evaluated. Part-of-speech tagging was implemented by TnT tagger, a statistical-based part-of-speech tagger, and Bi-LSTM tagger, a deep learning-based part-of-speech tagger. TnT tagger and Bi-LSTM tagger were trained on the Head-Tail tokenized corpus to measure the part-of-speech tagging accuracy. As a result, it showed that the Bi-LSTM tagger performs part-of-speech tagging with a high accuracy of 99.52% compared to 97.00% for the TnT tagger.

• Smart Media Journal
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• v.11 no.10
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• pp.89-96
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• 2022

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.1-12
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• 2020

In the medical field, numerical imbalance of data due to differences in disease prevalence is a common problem. It reduces the performance of a artificial intelligence network, leading to difficulties in learning a network with good performance. Recently, generative adversarial network (GAN) technology has been introduced as a way to address this problem, and its ability has been demonstrated by successful applications in various fields. However, it is still difficult to achieve good results in solving problems with performance degraded by numerical imbalances because the image resolution of the previous studies is not yet good enough and the structure in the image is modeled locally. In this paper, we propose a multi-scale conditional generative adversarial network based on attention mechanism, which can produce high resolution images to solve the numerical imbalance problem of chest X-ray image data. The network was able to produce images for various diseases by controlling condition variables with only one network. It's efficient and effective in that the network don't need to be learned independently for all disease classes and solves the problem of long distance dependency in image generation with self-attention mechanism.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.73-80
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• 2012

Recently, medical equipments are developed and used for diagnosis or studies. In addition, demand of techniques which automatically deal with three dimensional medical images obtained from the medical equipments is growing. One of the techniques is automatic bone segmentation which is expected to enhance the diagnosis efficiency of osteoporosis, fracture, and other bone diseases. Although various researches have been proposed to solve it, they are unable to be used in practice since a size of the medical data is large and there are many low contrast boundaries with other tissues. In this paper, we present a fast and accurate automatic framework for bone segmentation based on multi-resolutions. On a low resolution step, a position of the bone is roughly detected using constrained branch and mincut which find the optimal template from the training set. Then, the segmentation and the registration are iteratively conducted on the multiple resolutions. To evaluate the performance of the proposed method, we make an experiment with femur and tibia from 50 test knee magnetic resonance images using 100 training set. The proposed method outperformed the constrained branch and mincut in aspect of segmentation accuracy and implementation time.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.303-313
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• 2018

In order to provide quality services across international airports, airline personnel must rapidly and effectively develop and share knowledge. Combining components of adaptive structuration theory (AST) and media synchronicity theory (MST), a research framework was developed to convey three distinct stages of knowledge sharing. We use the grounded theory research method for the qualitative data collected from audio transcripts of employees learning how to use and work with company issued smartphones with push-to-talk functionalities. Data was collected from 33 operations personnel. The results of the content analysis are recorded for the elements of each of the three concepts of our research framework. During the social interaction stage, the content of the audio conversations shifts mainly from conflict management to task management; for media synchronicity, from quality to quantity; for productive outcomes, from efficiency to commitment. New insights are uncovered from our analysis of data from the field as users advance from learning how to use the mobile devices, to using the devices for managing knowledge for their work in the airline industry.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.506-518
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• 2015

Recently there are growing needs and demand to enhance 'Unity of knowledge' as the concept of "Creating new value through integration and convergence" is developing rapidly in many different areas in the society. This also has significant implication to education. Especially, it requires paradigm shift in terms of required capabilities and qualifications for the students with science major. To accommodate this trend, Natural Sciences and Engineering's College has been increasing convergence education which focus on cultivating creative and cooperative learning capabilities as well as acquiring fundamental knowledge of individual majors. However, convergence education developed and implemented by Sciences college or liberal education so far has been mechanical combination of knowledge from different academic fields - not effectively integrated and interdisciplinary education. Given this situation, this research is to develop and propose a "convergence education program based on the development of scientific and cultural contents" as an education tool to enhance capabilities to apply and re-create integrated knowledge as well as acquire and learn existing knowledge. Education program developed in this research aims to achieve two different and sequential capabilities. First is to understand 'Science and Technology' and 'Cultural Archetype' which would be essential and useful to create cultural contents. Second is to develop capabilities to convert this understanding into cultural contents - a storytelling capability. This education program is differentiated in that it defines cultural contents as a medium to converge and integrate science and technology and humanities. By leveraging the concept of cultural content and storytelling, this education program would be able to overcome restrictions of existing interdisciplinary approach. Also, this program would encourage students to try in-depth research and new applications, and develop logical and creative thinking.