• Geophysics and Geophysical Exploration
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• v.18 no.1
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• pp.31-34
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• 2015

A gravity meter has been used for exploring subsurface mineral resources and monitoring long-period events such as Earth tides. Recently, researchers found several other intriguing features that we could even detect large teleseismic earthquakes and monitor seismic ambient noise using gravimeters. The zero-length spring suspension technology gives the gPhone (Micro-g LaCoste) excellent low frequency sensitivity, which may have implications for investigating much longer-period natural events (e.g., Earth's hum, tsunami waves, etc.). In this study, we present preliminary results through temporary operation of the gPhone at Geumsan in South Korea for 9 months (Nov. 2008-Jul. 2009). The gPhone successfully recorded large teleseismic events and showed a clear seasonal variation of the Double frequency microseisms during its operation period.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.16-23
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• 2019

Fine particulate matter known as PM 2.5 refers to the atmospheric particulate matter that has a diameter less than 2.5 micrometer identified as dangerous element for human health and its concentration can provide us a clear picture about air dust concentration. Humans stay indoor almost 90% of their life time and also there is no official indoor dust concentration data, so our study is focused on measuring the indoor air quality. Indoor dust data monitoring is very important in hospital environments beside that other places can also be considered for monitoring like classrooms, cements factories, computer server rooms, petrochemical storage etc. In this paper, visible light communication system is proposed by Manchester encoding technique for electromagnetic interference (EMI)-free indoor dust monitoring. Important indoor environment information like dust concentration is transferred by visible light channel in wide range. An average voltage-tracking technique is utilized for robust light detection to eliminate ambient light and low-frequency noise. The incoming light is recognized by a photo diode and are simultaneously processed by a receiver micro-controller. We can monitor indoor air quality in real-time and can take necessary action according to the result.

• Geophysics and Geophysical Exploration
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• v.15 no.3
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• pp.144-149
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• 2012

Korea Polar Research Institute (KOPRI) has successfully installed two autonomous very broadband three-component seismic stations at the King George Island (KGI), Antarctica, during the 24th KOPRI Antarctic Summer Expedition (2010 ~ 2011). The seismic observation system is originally designed by the Incorporated Research Institutions for Seismology Program for Array Seismic Studies of the Continental Lithosphere Instrument Center, which is fully compatible with the Polar Earth Observing Network seismic system. The installation is to achieve the following major goals: 1. Monitoring local earthquakes and icequakes in and around the KGI, 2. Validating the robustness of seismic system operation under harsh environment. For further intensive studies, we plan to move and install them adding a couple more stations at ice shelf system, e.g., Larsen Ice Shelf System, Antarctica, in 2013 to figure out ice dynamics and physical interaction between lithosphere and cryosphere. In this article, we evaluate seismic station performance and characteristics by examining ambient noise, and provide operational system information such as frequency response and State-Of-Health information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.878-886
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• 2018

This study quantified the sounds of riffles and pools in natural rivers and conducted a comparative analysis of the frequency and sound pressure per flow velocity. The surveyed area was Namdaecheon basin in Yangyang-gun, Gangwon-do and the sounds of a total of 23 sites were analyzed. A hydro microphone was used to measure the sound and analyze the data using an acoustic analysis program. The location was also selected at places with minimal ambient noise and the measurement points were the depth of riffles and pools. The results revealed an average difference of 0.515 m/s for flow velocity at 8 riffles and 15 pools. The difference in sound pressure occurred due to the flow velocity. In the case of sound pressure, it was measured at an average of 176.8 dB for riffles and 168.2 dB for pools, demonstrating a difference of approximately 8.6 dB. Furthermore, in the case of maximum sound pressure, riffles showed a constant range between 200 Hz and 250 Hz, while the pools exhibited maximum sound pressure at various frequencies from 200 Hz to 1,000 Hz. This revealed the ecological stream reproduction, development of preferred sound sources for aquatic life, and design of structures.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.