• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.389-395
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• 2015

According to much research on cognitive science, the impact of the scene-context on human visual search in real-world images could be as important as the saliency. Therefore, this study proposed a method of Adaptive Control of Thought-Rational (ACT-R) modeling of visual search in real-world images, based on saliency and scene-context. The modeling method was developed by using the utility system of ACT-R to describe influences of saliency and scene-context in real-world images. Then, the validation of the model was performed, by comparing the data of the model and eye-tracking data from experiments in simple task in which subjects search some targets in indoor bedroom images. Results show that model data was quite well fit with eye-tracking data. In conclusion, the method of modeling human visual search proposed in this study should be used, in order to provide an accurate model of human performance in visual search tasks in real-world images.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.35.4-35
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• 2001

Image detection system for road traffic data analysis is a real time detection system using image processing techniques to get the real-time traffic information which is used for traffic control and analysis. One of the most important functions in this system is to match the coordinates of real world and that of image on video camera. When there in no way to know the exact position of camera and it´s height from the object. If some points on the road of real world are known it is possible to calculate the coordinates of real world from image.

• Journal of the Korean Society of Costume
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• v.61 no.9
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• pp.1-14
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• 2011

The purpose of this study was to understand the relationship between one's social-self image and Online Avatar image. Influence of these virtual images on one's attitude toward real world and commitment to the virtual world was examined. In addition, the gender difference was examined. A structural equation model with social self image as exogenous variable and influence of Avatar as endogenous variable was designed. Real and ideal Avatar images were the mediating variable in the model. Survey questionnaire was developed and data from 425 respondents were analyzed. Results indicated that the conceptual model was a good fit to the data. Respondents who perceived their social self-images importantly were likely to have real images of Avatars. Ideal image and real image had significant on commitment to virtual world and attitudes toward the real world. For male respondents, social self image had stronger influence on real image of Avatar and ideal image had stronger influence on commitment to virtual world than female respondents.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.19-26
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• 1998

In general, GIS provides a user with geographical information according to his requirements after a computer stores digitized spatial data from real scenes. Geographical information from digitized spatial data lacks of reality due to the exclusion of real scenes. However, Augmented Reality(AR) allows a user to see the real world with virtual objects superimposed upon or composited with the real world. Therefore it enhances a user's perception of the real world. In this paper, we present the development on 3D geographical information guide system using augmented reality that displays the real world with virtual objects such as building name, road name, etc., in real time.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.172-172
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• 2023

The use of low-cost IoT sensors for flow measurement in open channels has gained significant attention due to their potential to provide continuous and real-time data at a low cost. However, the accuracy and reliability of these sensors in real-world scenarios are not well understood. This study aims to compare the performance of low-cost IoT sensors in the laboratory and real-world conditions to evaluate their accuracy and reliability. Firstly, a low-cost IoT sensor was integrated with an IoT platform to acquire real-time flow rate data. The IoT sensors were calibrated in the laboratory environment to optimize their accuracy, including different types of low-cost IoT sensors (HC-SR04 ultrasonic sensor & YF-S201 sensor) using an open channel prototype. After calibration, the IoT sensors were then applied to a real-world case study in the Dorim-cheon stream, where they were compared to traditional flow measurement methods to evaluate their accuracy.The results showed that the low-cost IoT sensors provided accurate and reliable flow rate data under laboratory conditions, with an error range of less than 5%. However, when applied to the real-world case study, the accuracy of the IoT sensors decreased, which could be attributed to several factors such as the effects of water turbulence, sensor drift, and environmental factors. Overall, this study highlights the potential of low-cost IoT sensors for flow measurement in open channels and provides insights into their limitations and challenges in real-world scenarios.

• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Translational and Clinical Pharmacology
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• v.26 no.4
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• pp.155-159
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• 2018

For regulatory approval of a new drug, the most preferred and reliable source of evidence would be randomized controlled trials (RCT). However, a great number of drugs, being developed as well as already marketed and being used, usually lack proper indications for children. It is imperative to develop properly evaluated drugs for children. And expanding the use of already approved drugs for other indications will benefit patients and the society. Nevertheless, to get an approval for expansion of indications, most often with off-label experiences, for drugs that have been approved or for the development of pediatric indications, either during or after completing the main drug development, conducting RCTs may not be the only, if not right, way to take. Extrapolation strategies and modelling & simulation for pediatric drug development are paving the road to the better approval scheme. Making the use of data sources other than RCT such as EHR and claims data in ways that improve the efficiency and validity of the results (e.g., randomized pragmatic trial and randomized registry trial) has been the topic of great interest all around the world. Regulatory authorities should adopt new methodologies for regulatory approval processes to adapt to the changes brought by increasing availability of big and real world data utilizing new tools of technological advancement.

• Korean Circulation Journal
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• v.52 no.12
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• pp.853-864
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• 2022

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.880-882
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• 1999

Virtual Reality is a set of computer technologies which, when combined, provide an interface to a computer-generated world, and in particular, provide such a convincing interface that the user believes he is actually in a three dimensional computer-generated world. This computer generated world may be a model of a real-world object, such as a house; it might be an world that does not exist in a real sense but is understood by humans, such as a chemical molecule or a representation of a set of data; or it might be in a completely imaginary science fiction world. this paper describes the application of boiler digital control system MMI for power plant using virtual reality