• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.355-372
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• 2021

Recently, high-performance HMDs (Head-Mounted Display) are becoming wireless due to the growth of virtual reality technology. Accordingly, environmental constraints on the hardware usage are reduced, enabling multiple users to experience virtual reality within a single space simultaneously. Existing multi-user virtual reality platforms use the user's location tracking and motion sensing technology based on vision sensors and active markers. However, there is a decrease in immersion due to the problem of overlapping markers or frequent matching errors due to the reflected light. Goal of this study is to develop a multi-user virtual reality moving platform in a single space that can resolve sensing errors and user immersion decrease. In order to achieve this goal hybrid sensing technology was developed, which is the convergence of vision sensor technology for position tracking, IMU (Inertial Measurement Unit) sensor motion capture technology and gesture recognition technology based on smart gloves. In addition, integrated safety operation system was developed which does not decrease the immersion but ensures the safety of the users and supports multimodal feedback. A 6 m×6 m×2.4 m test bed was configured to verify the effectiveness of the multi-user virtual reality moving platform for four users.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1138-1148
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• 2021

In this study, we developed a 'shared-use mobility'-mounted local particulate matter monitoring terminal technology to measure the actual particulate matter concentration around me. As a mobile terminal device in the form of an IoT sensor platform, it is designed to be separated into a control module and a sensor module to minimize interference between sensors and to consider the optimal observation position of each sensor. As a result of the field test, it was confirmed that particulate matter was locally different depending on time and space even within the same area. In addition, it was confirmed that the concentration of particulate matter in the relevant section differed by up to 100 times compared to the surrounding area due to specific sources of particulate matter such as unpaved roads. In addition, we positively reviewed the applicability of the service in the real-time metaverse environment using this result. Through technological advancement and application of multiple shared-use mobilities, we expect to be able to provide new services for practical smart city air environment monitoring, such as localized particulate matter information, air pollution event information, and identification of causes of particulate matter.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.23-29
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• 2022

As the application field of radar is expanded and the bandwidth increases, the number of radar sensors operating at the same frequency is continuously increasing. In this paper, we propose a method of analyzing interference when two pulse doppler radars are operated at the same frequency with different waveform which are designed independently. In addition, we show that even for a previously designed LFM waveforms, the interference can be suppressed without affecting the performance by changing the sign of the frequency slope by increasing/decreasing, or by modulating the pulses by the different codes. The interference suppression by different slopes is more effective for similar waveform and the suppression by the codes increases as the number of pulses increases. We expect this result can be extended to the cases where multiple radars are operated at the same frequency.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.719-724
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• 2021

In this paper, we studied properties of smart farms that can automatically control not only the temperature and humidity but also the illumination to improve plant productivity. The smart farm was designed to allow the controllers to operate through Arduino by receiving input values from each sensor. In addition, to maximize the convenience of smart farm, the Bluetooth communication module is used to control the smart phone. The study confirmed that the automation function of smart farms can create an environment suitable for plants to grow.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.147-149
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• 2020

코로나바이러스의 세계 확산, 발병 이후 사람들의 실내활동 증가와 건강, 면역에 대한 관심은 많이 증가했다. 이에 맞춰 더욱 정교하고 바른 정보에 의한 스마트헬스케어 역시 관심이 증대되고 있다. 여기서 이야기하는 스마트헬스케어의 범위는 영상 장치를 비롯해 다양한 센서를 활용해 신체활동을 모니터링하고 분석하며 기존의 방식보다 더 객관적인 정보를 제공해 주는 것을 말한다. 위 기술과 대중의 관심을 바탕으로 하여 본 연구에서는 다중 모드 센서를 신체에 부착하여 신체활동을 모니터링 하는 시스템 개발을 목적으로 한다. 하드웨어 설계 부분에서 설계가 완성된 Arduino nano 33 Sense를 이용하여 스마트 헬스 실험 시간을 대폭 줄였다. 또한 운동과 같은 시계열 데이터를 분석하기 좋은 LSTM 기법을 채택하였으며, 개발된 모델을 추후 활용할 방안에 대해 논하였다.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.6
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• pp.620-626
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• 2023

Energy harvesting technology, which converts wasted energy sources in everyday life into usable electric energy, is gaining attention as a solution to the challenges of charging and managing batteries for the driving of IoT sensors, which are one of the key technologies in the era of the fourth industrial revolution. Hybrid energy harvesting technology involves integrating two or more energy harvesting technologies to generate electric energy from multiple energy conversion mechanisms. In this study, a hybrid energy harvesting device called TMPPEG (thermo-magneto-piezoelectric-pyroelectric energy generator), which utilizes low-grade waste heat, was developed by incorporating PVDF polymer piezoelectric components and optimizing the system. The variations in piezoelectric output and thermoelectric output were examined based on the spacing of the clamps, and it was found that the device exhibited the highest energy output when the clamp spacing was 2 mm. The voltage and energy output characteristics of the TMPPEG were evaluated, demonstrating its potential as an efficient hybrid energy harvesting component that effectively harnesses low-grade waste heat.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3709-3715
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• 2023

It is an important work to measure the dimensions of underwater spent fuel assemblies in the nuclear power industry during the overhaul, to judging whether the spent fuel assemblies can continue to be used. In this paper, a three dimensional reconstruction method for underwater spent fuel assemblies of nuclear reactor based on linear structured light is proposed, and the topography and size measurement was carried out based on the reconstructed 3D model. Multiple linear structured light sensors are used to obtain contour size data, and the shape data of the whole spent fuel assembly can be collected by one-dimensional scanning motion. In this paper, we also presented a corrected model to correct the measurement error introduced by lead-glass and water is corrected. Then, we set up an underwater measurement system for spent fuel assembly based on this method. Finally, an underwater measurement experiment is carried out to verify the 3D reconstruction ability and measurement ability of the system, and the measurement error is less than ±0.05 mm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.191-192
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• 2024

고령 인구의 지속적인 증가로 인해 고령자의 안전과 관련된 문제는 주요한 관심사 중 하나로 부상하고 있다. 특히, 고령자들 사이에서 자주 발생하는 낙상 사고는 심각한 건강 문제를 일으킬 수 있으며, 이를 예방하고 대응하는 것은 고령 인구의 삶의 질을 향상하는 데 중요한 역할을 한다. 본 연구는 8대의 카메라로 촬영된 영상과 센서 데이터를 통합한 낙상 감지 기법을 제안한다. 제안한 기법은 MediaPipe를 활용하여 Skeleton Keypoint를 추출하는 이미지 인식 기법과 센서 데이터에서 얻은 특징을 활용하는 센서 기반 기술을 결합하여 낙상 사고의 발생 및 방향을 효과적으로 감지할 수 있다. 이러한 결과를 바탕으로 본 연구는 향후 고령자들의 생활 안전성과 의료 시스템의 효율성을 높이는 데 이바지할 수 있을 것으로 기대한다.