• Smart Structures and Systems
• /
• v.22 no.2
• /
• pp.185-192
• /
• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.608-610
• /
• 2023

의료 분야에서 AI 모델의 활용이 증가하고 있지만, 모델의 예측 불확실성을 정확하게 평가하고 표현하는 것이 중요하다. 본 연구는 이러한 문제를 해결하기 위해 AI-driven 방식을 제안하며, 특히 의료 영상 변환 모델에 대한 불확실성 표현과 데이터 한계 극복 방법론을 제안한다. 제안된 AI-driven 안저영상 변환 모델은 기존 GAN과는 다르게 구조가 이루어져 있으며, 신뢰도가 낮은 영역을 구분하고 시각화하여 표현할 수 있다. 실험 결과, 제안된 방법은 기존 모델과 비교하여 영상 변환 성능이 크게 향상되었으며, 불확실성에 대한 정확도 평가에서도 AI-driven 방식이 높은 성능을 보인다. 결론적으로, 본 연구는 AI-driven 방식을 통해 의료 AI에서의 불확실성 표현의 가능성을 확인하였으며, 이 방식이 데이터의 한계와 불확실성을 극복할 수 있을 것으로 기대된다.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.36 no.3
• /
• pp.169-176
• /
• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.317-320
• /
• 2015

Documenting work-in-progress on construction sites using images captured with smartphones, point-and-shoot cameras, and Unmanned Aerial Vehicles (UAVs) has gained significant popularity among practitioners. The spatial and temporal density of these large-scale site image collections and the availability of 4D Building Information Models (BIM) provide a unique opportunity to develop BIM-driven visual analytics that can quickly and easily detect and visualize construction progress deviations. Building on these emerging sources of information this paper presents a pipeline for model-driven visual analytics of construction progress. It particularly focuses on the following key steps: 1) capturing, transferring, and storing images; 2) BIM-driven analytics to identify performance deviations, and 3) visualizations that enable root-cause assessments on performance deviations. The information requirements, and the challenges and opportunities for improvements in data collection, plan preparations, progress deviation analysis particularly under limited visibility, and transforming identified deviations into performance metrics to enable root-cause assessments are discussed using several real world case studies.

• Design & Manufacturing
• /
• v.16 no.1
• /
• pp.15-20
• /
• 2022

This paper is about elimination the situation in which gas sensor data becomes inaccurate due to temperature control when a semiconductor gas sensor is driven. Recently, interest in semiconductor gas sensors is high because semiconductor sensors can be driven with small and low power. Although semiconductor-type gas sensors have various advantages, there is a problem that they must operate at high temperatures. First temperature control was configured to adjust the temperature value of the heater mounted on the gas sensor. At that time, in controlling the heater temperature, gas sensor data are fluctuated despite supplying same gas concentration according to the temperature controlled. To resolve this problem, gas and temperature are extracted as a data. And then, a relation function is constructed between gas and temperature data. At this time, it is included low pass filter to get the stable data. In this paper, we can find optimal gain and parameters between gas and temperature data by using genetic algorithm.

• Journal of the Microelectronics and Packaging Society
• /
• v.31 no.2
• /
• pp.45-53
• /
• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• Structural Monitoring and Maintenance
• /
• v.6 no.2
• /
• pp.125-145
• /
• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Construction Engineering and Management
• /
• v.19 no.2
• /
• pp.63-68
• /
• 2018

• Computational Structural Engineering
• /
• v.35 no.1
• /
• pp.11-16
• /
• 2022

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.253-253
• /
• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.