• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.15-22
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• 2014

This paper presents the implementation of a control system of pellet boiler using wood pellet as carbon neutral material. The system also has the additional features to provide remote controlling and monitoring based on home networking technology through either public switched telephone networks or mobile communication networks. It consists of three kinds of sub-modules; a main controller provides basic and additional features such as a setting of temperature, a supplying of wood pellet, a controlling of ignition and fire-power, and a removing of soot. The second is temperature controller of individual rooms which is connected to the main controller through RS-485 links. And interface modules with PSTN and mobile networks can support remote controlling and monitoring the functions. The test results under the heating area of $172m^2$ show a thermal efficiency of 93.6%, a heating power of 20,640kcal/hr, and a fuel consumption of 5.54kg/hr. These results are superior to those of the conventional pellet boilers. In order to obtain the such high performance, we newly applied a 3-step ignition flow, a flame detection by $C_dS$ sensor, and a fire-power control by fine controlling of shutter to our pellet boiler.

• Cartoon and Animation Studies
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• s.25
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• pp.41-63
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• 2011

Many educational facilities have been formed due to the nation's revival policy in the animation industry since about 1995. Owed to the active financial support of the nation, the production industry rapidly vitalized, production technology rapidly advanced, and a large workforce is being passed on into society through educational institutions. The reality of many developing countries appearing to be putting emphasis on the industrialization of animation, similar to our country, is becoming a great pressure on us industrially. It is never easy to develop a certain field into a globally competitive industry in a short period of time. Our countermeasure, pursuant to these international circumstances, lies in innovation and creativity that has broken away from the existing methods of production, and mass production of high quality animation specialists. This paper is a new educational proposition for the consolidation of national competitiveness. Animating, the core of producing an animation, completely depends on the animator's artistic and technical ability. In order to supplement the existing methods of studying by theory and make up for the biggest weak point, which is the lack of "on the scene" learning contents, I propose incorporating movements based on frequently appearing characters in popular animations and acting them out slowly and including the "Slow Motion" kinetic effect, a way of enabling someone to learn and sense astrodynamic fundamental principles by oneself. It is a new method of learning movement, a plan made to achieve sensual performance gestures, and an improvement in direction for students who wish to become animators in the future.

• Korean Journal of Applied Biomechanics
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• v.15 no.3
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• pp.21-30
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• 2005

The purpose of this study is helps to make full use for perfect performance by grasping the defects of Tichonkich motion performed by athlete CSM For this, the study analyzed kinematical variables through Tichonkich motions performed at the first selection competition(1st trial) and final selection competition(2nd trial) for the dispatch to the 28th Athens Olympic Games using the three-dimensional cinematographical method with a high-speed video camera, and obtained the following results. 1. During Tichonkich motion, the execution time of up swing and the right hand moving to the left bar was shorter in the 2nd trial than the 1st one, while the execution time of down swing, the support of the left bar and the right hand moving to the right bar was longer in the 2nd trial than the 1st trial. 2. The horizontal position of COG in the 2nd trial was -35cm in the 1st stage, 42cm in the 3rd stage and 29cm in the 4th stage, that is, it showed a great swing focused on the circular movement compared to the 1st trial, while the vertical position of COG was -59cm in the 2nd stage, that is, it showed a small swing focused on a up and down movement. Also the 5th stage vertical position was 98cm, and the 6th stage vertical position was 95cm in the 2nd trial which were higher than those of the 1st trial, so it has provided magnificence required in the modern gymnastics. 3. And it was indicated that the horizontal velocity at the down swing phase proceeded forward more rapidly in the 2nd trial than that in the 1st trial, and the reverse ascent made a rapid vertical rise lessening left and right velocity change. And in the 5th stage, the 2nd trial was kept very slower in horizontal, vertical and left and right velocity that in the 1st trial, so it reached a handstand with leisurely movement. 4. In the 2nd trial, shoulder joint of the 1st, 2nd, 3rd stages kept a larger angle than that in the 1st trial, that is, it made a great swing while in the 1st trial, it showed a swing movement dependent on kick movement by the flexion and extension of hip joint. Also in the 2nd trial, the body formed a vertical posture with both hands supporting the left bar and hip joint was kept larger as $198^{\circ}$ and $190^{\circ}$ in the 5th and 6th stage than that in the 1st trial, so it made a handstand with the body uprightly stretched out, and magnificent and stable movement.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.417-425
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• 2021

This study was conducted to evaluate the characteristics of major selection of college students majoring in culinary. By conducting an Importance-Performance Analysis(IPA) through students who are currently majoring in a culinary major, it is intended to increase student satisfaction, student loyalty, the enrollment rate and to present the direction the college should pursue. The questionnaire was conducted for 4 weeks from June 22, 2020 to July 19, 2020, the results are as follows. As a result of the t-test (paired sample t-test) for 23 attributes, the average value of importance was 4.0765, the average value of satisfaction was 3.5091, showing high importance, the attributes considered important by item were 'educational facilities (4.50)', 'school welfare (4.50)', the attributes having the highest satisfaction with experience after selecting a major were 'aptitude and conformity (3.94)', 'future hope and concordance (3.91)'. The IPA analysis results on the major selection attributes of college students majoring in culinary are as follows. First, In the first quadrant, 11 attributes including 'aptitude and conformity' appeared, Second, In the second quadrant, 5 attributes including 'employment support' appeared. Third, In the third quadrant, 5 attributes including 'college scholastic ability score' appeared, Finally, In the fourth quadrant, 2 attributes including 'experience in major field' appeared.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.161-183
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• 2022

Opinion polls have become a powerful means for election campaigns and one of the most important subjects in the media in that they predict the actual election results and influence people's voting behavior. However, the more active the polls, the more often they fail to properly reflect the voters' minds in measuring the effectiveness of election campaigns, such as repeatedly conducting polls on the likelihood of winning or support rather than verifying the pledges and policies of candidates. Even if the poor predictions of the election results of the polls have undermined the authority of the press, people cannot easily let go of their interest in polls because there is no clear alternative to answer the instinctive question of which candidate will ultimately win. In this regard, we attempt to retrospectively grasp public opinion on the 20th presidential election by applying the 'YouTube Analysis' function of Sometrend, which provides an environment for discovering insights through online big data. Through this study, it is confirmed that a result close to the actual public opinion (or opinion poll results) can be easily derived with simple YouTube data results, and a high-performance public opinion prediction model can be built.

• Korean small business review
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• v.43 no.4
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• pp.173-209
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• 2021

Start-up accelerators are a new type of investor that provide mentoring, education and seed capital to start-ups for a fixed duration and receive a certain stake in them in return. They help start-ups achieve successful commercialization. With increase in performance visibility, the number of private and public sector accelerators rose across domestic and international markets. Private sector accelerators are established and operated by private entities while public sector accelerators are established and operated by the government. Both play complementary roles that are becoming increasingly important to start-ups. Therefore, this study aims to examine the differences in major operational goals and investment determinants between private and public sectors and to understand their implications. The results show that the private sector prioritizes profit generation through the investment, while the public sector aims to contribute to the development of high-growth start-ups, and create region-specific and technology-specific start-up ecosystems. Additionally, both groups consider customer needs the most important determinant. Public groups are more conservative in investments and tend to place importance on objective indicators such as patents, partners, mentors, and co-founders. Conversely, private groups value the capabilities of founders and their ease of collaboration with accelerators. These findings can help start-ups get support from public or private accelerators more easily. It will also help public and private accelerators refine the criteria for selecting start-ups.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.