• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.57-64
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• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.258-264
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• 2022

The smart farm for livestock, in which information and communication technology (ICT) is combined with livestock farm, is mostly based on the cloud computing paradigm. A cloud-based smart livestock farm has disadvantages such as increased response time, burden on cloud resource caused by the increased number of IoT sensors, traffic burden on the network, and lack of failure resilience mechanisms through collaboration with adjacent IoT devices. In this paper, with these problems in mind, we propose an IoT collaboration system based on edge computing. By using the relatively limited computing resources of the edge device to share the cloud's web server function, we aim to reduce the cloud's resources needed and improve response time to user requests. In addition, through the heartbeat-based failure recovery mechanism, IoT device failures were detected and appropriate measures were taken.

• Composites Research
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• v.22 no.6
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• pp.1-6
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• 2009

This paper presents an experimental study on the piezoresistive behavior of nanocomposite strain sensors subjected to various loading modes and their capability to detect structural deformations and damages. The electrically conductive nanocomposites were fabricated in the form of a film using various types of thermoplastic polymers and multi-walled carbon nanotubes (MWNTs) at various loadings. In this study, the nanocomposite strain sensors were bonded to a substrate and subjected to tension, flexure, or compression. In tension and flexure, the resistivity change showed dependence on measurement direction, indicating that the sensors can be used for multi-directional strain sensing. In addition, the sensors exhibited a decreasing behavior in resistivity as the compressive load was applied, suggesting that they can be used for pressure sensing. This study demonstrates that the nanocomposite strain sensors can provide a pathway to affordable, effective, and versatile structural health monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.320-320
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• 2021

홍수피해가 빈발하는 도시 및 소규모 산지 유역에서와 같이 지체시간이 짧은 유역에서 국지적으로 발생하는 돌발홍수는 우량계와 기존 하천유역 예보시스템만으론 예보가 불가능하다. 동일한 강우에서도 지역에 따라 침수시간이나 침수심이 달라지기 때문에 정확한 돌발홍수예보를 위해서는 지역에 따른 침수특성과 유속특성을 달리 고려해야 한다. '골든타임 확보를 위한 유역 시공간 상세 홍수예보기술 개발(환경부)'에서 개발한 '국지 돌발홍수예측 시스템'은 지역별 검증된 침수특성과 유속특성의 관계식을 산정하여 돌발홍수예보 기준을 설정하였다. 그리고 도달시간이 짧은 도시 및 산지에서 홍수예보 선행시간을 확보하기 위해 강우레이더 기반 돌발홍수 예측 시스템을 구축하여 시범 운영 중이다. 그러나 도시·산지 중소하천유역 등 홍수예보 취약지역에 대한 돌발홍수예보 정확도를 제고하기 위해서는 기 설정된 돌발홍수위험 예보 기준을 정밀하게 평가·검증·개선 할 수 있는 실증 체계가 반드시 필요하다. 이러한 배경에서 본 연구에서는 2021년부터 3개년 동안 홍수예보 취약지역에 강우레이더와 경제적 IoT 관측센서 정보를 기반으로 돌발홍수예보 실증기술을 개발하여 전국 돌발홍수예보 실용화 기반 구축하고자 한다. 홍수피해 취약지역인 도심지, 산지·계곡, 해안지역에 실증 테스트베드를 선정하고 강우레이더-IoT 실증 관측망을 구축하여 돌발홍수예보 기술 실증과 돌발홍수 위험기준 설정 가이드라인을 마련하고자 한다. 더불어 도시 중소하천유역 홍수예보 활용을 위한 소형강우레이더 강우량 정확도 개선 기술 개발과 홍수기 강우레이더 기반 홍수예보 관-연 협업 시범 운영을 추진할 계획이며, 최종적으로는 강우레이더와 IoT 정보 기반 돌발홍수 실증 시스템을 구축 운영하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.380-380
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• 2020

가뭄 피해 극복을 위한 인공 함양지 통합관리시스템의 일부로써 지표수-지하수 연계 특성 분석용 의사결정을 전달하는 인공지능 스마트 계측기의 필요성이 꾸준히 제기되어 왔으나 실용성과 효율성을 동시에 갖춘 계측기는 시장에 출시되지 않았다. 기존의 계측기는 단순 측정이 목적이었으며 분석을 위해서는 일정 기간 직접 계측하여 분석하거나, 계측데이터를 원격 망을 통하여 서버로 전송하고 관리자가 데이터를 해석하는 방식을 취하였다. 또한, 수질 계측과 수질의 미소 변동성을 동시에 계측하여 수질 변화상태를 판단 할 수 있는 수질 계측기는 상품화되지 않아 다목적 수질 분석에 한계점을 갖고 있다. 이러한 한계점이 기존의 지하수 수질 계측기로는 불가능한 수중 라돈을 채수 없이 계측 가능하도록 하고, 순간 수질 변화 및 수질 변화 요인분석이 가능한 계측을 위하여 라돈, 전도도, 수위, 수온 및 필름형 pH 센서를 개발하여 적용한 다항목 계측기로 통합하는 연구가 필요한 이유이다. 개발한 계측기는 빅데이터 기반의 지능형 수질 변동성 분석 알고리즘을 내장하고 수직 깊이 방향의 다중심도 계측이 가능하도록 핵심적인 통신 연결성을 확보하였고 다양한 수질에서 견딜 수 있으며 특히 인공함양에서 발생하는 철, 망간에 부식되지 않는 재질을 이용하여 설계한 '지표수-지하수 연계 특성 분석용 다심도 및 인공지능 스마트 계측장치'이다. 본 장치는 기존 지하수 수질 계측기에서는 불가능하였던 순간 수위변화 및 수위변화 요인분석이 가능한 계측을 위하여 초당 측정 샘플링 주파수(10Hz)를 높인 계측회로를 개발하여 적용하였다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.639-647
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• 2023

A variety of communications are developed and advanced by integration of wireless and wire connections with heterogeneous system. Traditional technologies are mainly focus on information technology based on computer techniques in the field of industry, manufacture and automation fields. As new technologies are developed and enhanced with traditional techniques, a lot of new applications are emerged and merged with existing mechanism and skills. The representative applications are IoT(Internet of Things) services and applications. IoT is breakthrough technologies and one of the innovation industries which are called 4 generation industry revolution. Due to limited resources in IoT such as small memory, low power and computing power, IoT devices are vulnerable and disclosed with security problems. In this paper, we reviewed and analyzed security challenges, threats and requirements under IoT service.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.556-558
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• 2022

This paper proposes a CanSat system with a vehicle tracking function based on Jetson Nano, a high-performance small computer capable of operating artificial intelligence algorithms. The CanSat system consists of a CanSat and a ground station. The CanSat falls in the atmosphere and transmits the data obtained through the installed sensors to the ground station using wireless communication. The existing CanSat is limited to the mission of simply transmitting the collected information to the ground station, and there is a limit to efficiently performing the mission due to the limited fall time and bandwidth limitation of wireless communication. The Jetson Nano based CanSat proposed in this paper uses a pre-trained neural network model to detect the location of a vehicle in each image taken from the air in real time, and then uses a 2-axis motor to move the camera to track the vehicle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.