• KIPS Transactions on Computer and Communication Systems
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• v.7 no.4
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• pp.103-110
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• 2018

Services that enhance user convenience by using various IoT devices are increasing with the development of Internet of Things(IoT) technology. Also, since the price of IoT sensors has become cheaper, companies providing services by collecting and utilizing data from various sensors are increasing. The smart IoT home system is a representative use case that improves the user convenience by using IoT devices. To improve user convenience of Smart IoT home system, this paper proposes a method for the control of related devices based on data analysis. Internal environment measurement data collected from IoT sensors, device control data collected from device control actuators, and user judgment data are learned to predict the current home state and control devices. Especially, differently from previous approaches, it uses deep neural network to analyze the data to determine the inner state of the home and provide information for maintaining the optimal inner environment. In the experiment, we compared the results of the long-term measured data with the inferred data and analyzed the discrimination performance of the proposed method.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.296-301
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• 2019

Artificial intelligence is closely linked to our real lives, leading innovation in various fields. Especially, as a means of transportation possessing artificial intelligence, autonomous unmanned vehicles are actively researched and are expected to be put into practical use soon. Autonomous cars and autonomous unmanned aerial vehicles are required to equip accurate navigation system so that they can find out their present position and move to their destination. At present, the navigation of transportation that we operate is mostly dependent on GPS. However, GPS is vulnerable to external intereference. In fact, since 2010, North Korea has jammed GPS several times, causing serious disruptions to mobile communications and aircraft operations. Therefore, in order to ensure safety in the operation of the autonomous unmanned vehicles and to prevent serious accidents caused by the intereference, rapid situation judgment and countermeasure are required. In this paper, based on big data and machine learning technology, we propose a countermeasure system for GPS interference that supports decision making by applying John Boyd's OODA loop cycle (detection - direction setting - determination - action).

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.3
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• pp.311-318
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• 2018

In accordance with the stipulations of the STCW Convention, simulation training has been enforced in order to develop practical skills so as to prevent accidents by predetermining the risks in special marine environments. Simulation training is a useful way to acquire navigation abilities, and can continuously measure the ability of a trainee by applying an appropriate evaluation. However, the result of training is evaluated by the instructor's subjective judgment without quantitative criteria. Therefore, this study aims to quantitatively evaluate the effectiveness of simulation training. For this purpose, evaluation items were derived by analyzing legal standards, earlier studies, and the current status of MET institutions. The simulations were then performed three times in the same scenarios and analyzed the results. As a result, it has been shown that the objectively analyzed ability to keep the route and to make safe passage with other vessel, as well as subjectively evaluated ability by the apprentice officer has been improved as training progressed. Through the evaluation of simulation training results, it can be derived that simulation education needs supplementation, and can be provided as a basic form of data to quantify the evaluation results of the simulation training in the future.

• The Mathematical Education
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• v.46 no.4
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• pp.407-421
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• 2007

The purpose of this study is to identify the item characteristics that are supposed to affect item difficulty and to estimate the regression equations for predicting item difficulty of mathematics in the College Scholastic Ability Test(CSAT). We selected six variables related to item characteristics based on learning theories: contents, cognitive domain, novelty, item type, number of concepts, and the amount of computation. With data of the CSAT mathematics test administered in 2004-2006, item difficulty was regressed on the six variables, the location of an item, and the item writer's judgment on difficulty. The novelty of an item was found to be a statistically insignificant variable in explaining item difficulty. Four regression equations with different sets of independent variables could explain $70%{\sim}80%$ of the item difficulty variance and were validated as predicting item difficulty of the mock CSAT in 2006.

• Journal of Oriental Neuropsychiatry
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• v.26 no.3
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• pp.201-210
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• 2015

This study aims to investigate the specific functions of the five spirits based on the characteristics of five phases. In Korean medicine, the mind is classified into five spirits and seven emotions. The spirits (hon, sin, ui, baek, ji) are a way of analyzing of people's mental structures, and they are affected by each other, influencing life activities both directly and indirectly. They are also related to the five viscera and come into their own functions through the characteristics of the five phases that are assigned to the viscera. Sin is the main agent of mental activity that is normal, correct, and perfect, and it directs the other four. Hon is activity that is exposed to the outside from mental and physical aspects such as planning, creative thinking, creating, judgment, speech, and emotional expression. Baek is internal activity, such as obtaining information, learning, seeing, hearing, smell, taste, and touch. Ui is meant to decide between new and already saved information based on comparative analysis. Ji is the activity of making the final decision and saving it in ui. Based on the above, we suppose that the five spirits' functions match the characteristics of the five phases.

• Women's Health Nursing
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• v.26 no.1
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• pp.19-27
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• 2020

Purpose: This study was conducted to develop a simulation-based postpartum care education program for women with postpartum hemorrhage and to verify the effects of the program on postpartum care. Methods: This program was developed according to the ADDIE model of instructional system design, which consists of analysis, design, development, implementation, and evaluation phases. This quasi-experimental study used a non-equivalent control group pre- and post-test design, and data were collected from April 23 to May 4, 2015. To verify the effects of the program, 33 nursing students in the experimental group participated in a simulation program, whereas 31 students in the control group were given a case study. Results: The experimental group had statistically significantly higher scores for clinical performance (t=-4.80, p<.001), clinical judgment (t=-4.14, p<.001), and learning satisfaction (t=-10.45, p<.001) than the control group. Conclusion: The results of this study indicate that the simulation-based postpartum care education program for women with postpartum hemorrhage was effective for developing students' competency, implying that a similar program should be integrated into the clinical training component of the maternal nursing curriculum.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.13-17
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• 2019

Currently, the sports market continues to grow every year, and among them, professional baseball's entry income is larger than the rest of the professional league. In sports, strategies are used differently in different situations, and the analysis is based on data to decide which direction to implement. There is a part that a person misses in an analysis, and there is a possibility of a false analysis by subjective judgment. So, if this data analysis is done through artificial intelligence, the objective analysis is possible, and the strategy can be more rationalized, which helps to win the game. The most popular baseball to be applied to artificial intelligence to analyze athletes' strengths and weaknesses and then efficiently establish strategies to ease the competition. The data applied to the experiment were provided on the KBO official website, and the algorithms for forecasting applied linear regression. The results showed that the accuracy was 87%, and the standard error was ±5. Although the results of the experiment were not enough data, it would be possible to effectively use baseball strategies and predict the results of the game if the amount of data and regular data can be applied in the future.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.354-361
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• 2023

Most of spinal diseases are diagnosed based on the subjective judgment of a specialist, so numerous studies have been conducted to find objectivity by automating the diagnosis process using deep learning. In this paper, we propose a method that combines segmentation and feature extraction, which are frequently used techniques for diagnosing spinal diseases. Four models, U-Net, U-Net++, DeepLabv3+, and M-Net were trained and compared using 1000 X-ray images, and key-points were derived using Douglas-Peucker algorithms. For evaluation, Dice Similarity Coefficient(DSC), Intersection over Union(IoU), precision, recall, and area under precision-recall curve evaluation metrics were used and U-Net++ showed the best performance in all metrics with an average DSC of 0.9724. For the average Euclidean distance between estimated key-points and ground truth, U-Net was the best, followed by U-Net++. However the difference in average distance was about 0.1 pixels, which is not significant. The results suggest that it is possible to extract key-points based on segmentation and that it can be used to accurately diagnose various spinal diseases, including spondylolisthesis, with consistent criteria.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.85-92
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• 2009

As World Wide Web is more popularized nowadays, the environment is flooded with the information through the web pages. However, despite such convenience of web, it is also creating many problems due to uncontrolled flood of information. The pornographic, violent and other harmful information freely available to the youth, who must be protected by the society, or other users who lack the power of judgment or self-control is creating serious social problems. To resolve those harmful words, various methods proposed and studied. This paper proposes and implements the protecting system that it protects internet youth user from harmful contents. To classify effective harmful/harmless contents, this system uses two step classification systems that is harmful word filtering and SVM learning based filtering. We achieved result that the average precision of 92.1%.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.62-74
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• 2023

In response to diverse external stimuli, sensory receptors generate spiking nerve signals. These generated signals are transmitted to the brain along the neural pathway to advance to the stage of recognition or perception, and then they reach the area of discrimination or judgment for remembering, assessing, and processing incoming information. We review research trends in neuromorphic sensory perception technology inspired by biological sensory perception functions. Among the various senses, we consider sensory nerve decoding technology based on sensory nerve pathways focusing on touch and smell, neuromorphic synapse elements that mimic biological neurons and synapses, and neuromorphic processors. Neuromorphic sensory devices, neuromorphic synapses, and artificial sensory memory devices that integrate storage components are being actively studied. However, various problems remain to be solved, such as learning methods to implement cognitive functions beyond simple detection. Considering applications such as virtual reality, medical welfare, neuroscience, and cranial nerve interfaces, neuromorphic sensory recognition technology is expected to be actively developed based on new technologies, including combinatorial neurocognitive cell technology.