• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.1
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• pp.94-101
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• 2019

Most of the existing rail structures have undergone a lot of aging since a considerable period of time has passed from completion. In particular, among existing railway bridges, many of the plate girder bridges are older bridges that have lived 40 to 60 years or more. Since the treadmill is directly connected to the girder without the ballast, the running load of the vehicle is directly transmitted to the bridge. Therefore, the shock and noise applied to the bridge are larger than those of the ballast bridge, and the dynamic shock and vibration are also relatively large. Therefore, it is very urgent to develop appropriate maintenance, repair and reinforcement technology for existing steel plate bridge. In this study, the authors introduced the characteristics of embedded rail (ERS) developed for improving the performance of the existing plate girder bridge and the techniques solving the vibration and noise problems. In order to evaluate the vibration and noise reduction performance of ERS, a non-ballast plate girder bridge with 5m length of sleepers installed and a plate girder bridge with ERS were fabricated. And, then, the vibration response generated under the same excitation condition was measured and analyzed. Also, the radiated noise analysis was performed using the vibration response data obtained from the experiment as the input data of the acoustic analysis model. As a result of experiments and analyses, it was confirmed that the plate girder bridge's vibration using ERS was reduced by 15.0~18.8dB and the average noise was reduced by 7.7dB(A) more than the non-ballast bridge.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.123-131
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• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.1
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• pp.45-55
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• 2022

Because boundary confinement details proposed in the current design standards are significantly inferior in workability and production quality, it is necessary to develop boundary confinement details of RC structural walls that are capable of ensuring seismic performance and workability. With the recent development of the wire rod manufacturing technology, various pre-fabricated continuous hoop details can be manufactured. In this study, an analysis was conducted on the moment-curvature relationship of RC structural walls to which the pre-fabricated continuous hoop details were applied. According to the nonlinear cross-section analysis, the RC structure wall to which the details of the pre-fabricated continuous hoop details are applied can ensure seismic performance as the area of the pre-fabricated continuous hoop increases. Based on these research results, when applying the pre-fabricated continuous hoop in detail, it is necessary to secure the area of the pre-fabricated continuous hoop as much as the area of the existing boundary confinement.

• Tunnel and Underground Space
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• v.32 no.5
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• pp.298-311
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• 2022

Some problems, such as aging workers, a decreased population due to a low birth rate, and shortage of skilled workers, are rising in construction sites. Therefore research for smart construction technology that can be improved for productivity, safety, and quality has been recently developed with government support by replacing traditional construction technology with advanced digital technology. In particular, the motor grader that mainly performs road surface flattening is a construction machine that requires the application of automation technology for repetitive construction. It is predicted that the construction period will be shortened if the construction automation technology such as trajectory tracking, automation work, and remote control technology is applied. In this study, we introduce the hardware and software architecture of the smart motor grader to apply unmanned and automation technology and then analyze the traditional earthwork method of the motor grader. We suggested the application plans for the path pattern and blade control method of the smart motor grader based on this. In addition, we verified the performance of waypoint-based path-following depending on scenarios and the blade control's performance through tests.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.305-305
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• 2023

수문조사기기 검정은 강수량, 수위, 유량 등과 같은 수문자료를 관측하는 수문조사기기가 대상지역의 수문상황을 정확하게 관측하는지를 검사하는 일련의 과정으로 「수자원의 조사 계획 및 관리에 관한 법률」 제12조에 법적 기반을 두고 있다. 검정 대상은 강수량, 수위, 유속, 유사량, 토양수분량, 증발산량, 증발량 측정기기 총 7종이며, 환경부장관으로부터 한강홍수통제소가 검정업무를 위임받고, 한국건설기술연구원과 한국수자원조사기술원이 위탁받아 운영중에 있다. 최근에는 증발산량, 토양수분량 및 유량 측정기기기 등이 첨단화되어 기존 검정 방식에 대한보완 및 신설에 대한 요구가 증가하고 있다. 특히, 유량 측정시 기존에 사용하였던 회전식 유속계는 ADCP(Acoustic Doppler Current Profiler) 유량측정기기로 대체되어 활용률이 2013년 24%에서2021년 67%로 약 2.8배 급격히 증가하였다. 하지만 수문조사기기 검정 관련 고시 내 ADCP에 대한검사방법 및 허용오차 등의 규정이 부재하여 수문조사기기의 검정 공백이 발생하는 등의 문제가 존재하고 있다. 이에 본 연구에서는 ADCP 운영 및 기술 현황, 현행 법령, 국외 사례 등을 검토하여 ADCP 유량측정기기의 검사방법 및 허용오차에 대한 방안을 제시하고자 한다. ADCP 검사방법은 총 5단계로 외관검사, 자가진단 검사, 온도센서 검사, 수심측정 검사, 유량비교측정 검사에 따라 검정을 실시한다. 첫 번째 외관검사시에는 기기 외관과 센서 등 물리적 손상을 점검하고, 두 번째 자가진단 검사에서는 센서 변환 매트릭스 값, 수신부 센서 테스트, RAM/ROM 테스트, 통신 테스트 등에 관한 정상값 산출 여부를 확인한다. 세 번째 온도센서 검사에서는 검증용 온도센서를 이용한 값과 ADCP에 부착된 온도센서 값과 차이를 확인하고 ±2℃초과시 재검사 또는 적절한 조치를 취한 후 다음 단계의 검사를 진행한다. 네 번째 수심측정 검사에서는 수조 내 수심 측정을 확인하여 실제 수심과의 오차를 확인하고 ±1% 초과시 재검사 또는 적절한 조치 후 다음 검사를 실시한다. 유량비교 측정검사에서는 각 기기 간의 평균유량의 상대오차를 평가하는 것으로 ±5%미만에는 합격, ±5이상 ±10%미만에서는 재검사, ±10%이상에서는 공장수리를 권고하도록 하고, 1~5 단계의 검사를 통과한 기기를 대상으로 인증서를 발급하도록 한다. 유량비교 측정검사시에는 매년 ADCP를 사용하는 일반기업 및 공공기관 등이 모여 ADCP의 성능을 상호간 비교하는 'ADCP 기술협력 워크숍'을 확장하여 실시할 수 있다. 각 검사 단계의 허용오차는 USGS 또는 제조사 기준과 2022년 ADCP 기술협력 워크숍 성능검사 분석 결과를 토대로 하였다. 본 ADCP 검정 방안은 향후 ADCP 모델별로 단계별 시범 검토를 통해 세부사항에 대한 제시가 필요하며, 온도센서 검사, 수심측정 검사, 유량 비교측정검사에 대한 허용오차에 대한 타당성에대한 검증 및 검토가 이루어져야 할 것으로 사료된다.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.85-99
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• 2022

Dryers becoming commercially available for experimental and industrial use are classified to general drying oven, hot-air dryer, vacuum dryer, freezing dryer, etc. and kinds of them are various from the function, size and volume, etc. But the moisture measurement is not applied although it is important factor for the quality control and the performance improvement of products, and then now is very passive because the weight is weighed arbitrarily after dry-end. Generally the method for measuring moisture is divided by a direct measurement method and a indirect measurement method, and the former such as the change of weight or volume on the front and rear of separation of moisture, etc. is mainly used. Relatively a indirect measurement is very limited to apply due to utilize measurement apparatuses using temperature conductivity and micro-wave etc. In this research, we easily designed the moisture measurement system using the open-source based Arduino, and monitored moisture fluctuations and weight profiles in the real-time without the effect of external environment. Concretely the temperature-humidity and load cell sensors were packaged into a drying oven and the various change values were measured, and their sensors capable to operate 60℃ and 80℃ were selected to suitable for the moisture sensitive materials and the food dry. And also the performance safety using the organic samples of banana, pear, sawdust could be secured because the changes of evaporation rate as the dry time and temperature, and the measurement values of load cell appeared stable response characteristics through repeated experiments. Hereafter we judge that the reliability can be improved increasingly through the expansion of temperature-humidity range and the comparative analysis with CFD(Computational Fluid Dynamics) program.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.