The purpose of this study is to evaluate the applicability of an unsupervised outlier-detection method as a surrogate safety measure (SSM) to estimate the effect of AI-based Bike-Safe monitoring system. An SSM that utilizes near-miss data immediately before an accident occurs must be developed to compensate for inadequate bicycle accident data and missing reports. In particular, the omission level of accident reports related to bicycle users is higher on bicycle paths, which implies that the importance of an SSM in safety management is much greater than in the general road environment. Therefore, the unsupervised outlier-detection method was set as the SSM because it can be learned without a label, is suitable for streaming data, and is generalizable under limited data. Additionally, the DeepAnT(deep learningbased anomaly detection) model was selected as the most appropriate time-series outlier-detection method. Using the time-series prediction module of the learned DeepAnT model, we analyzed the frequency of outliers or avoidance behaviors based on a linear relationship between estimated and observed values. The history data of the acceleration change rate of each bicycle were applied to the DeepAnT model to evaluate the possibility of using alternative safety indicators. Thus, those data are expected to be applicable as an alternative safety indicator for bicycle paths.

최근 고도화된 딥러닝 모형을 이용하여 하천 수질에 영향을 줄 수 있는 과도한 조류(algae) 발생을 예측하는 연구에 대한 관심이 지속되고 있으며, 모형의 구축에 사용되는 현장 측정 자료의 특성상 다양한 이상치를 포함할 수 있어 데이터의 이상치 관리 필요성이 높아지고 있다. 본 연구에서는 현장 자료의 이상치가 딥러닝 모형의 성능에 미치는 영향을 분석하기 위해 딥러닝 Long Short-Term Memory(LSTM) 모형을 이용하여 하천 조류 발생을 정량적으로 평가하는 지표인 클로로필-a를 예측하는 모형을 구축하였으며, 10%의 이상치를 포함한 자료와 이상치가 포함되지 않은 원본 자료로 학습된 모형의 성능을 비교하였다. 또한 딥러닝 기반 이상치 탐지 알고리즘인 Autoencoder(AE)를 이용하여 이상치를 제거한 후 모형의 성능에 미치는 영향을 비교하였다. 분석 결과 이상치를 포함하지 않은 자료로 학습된 Base 모형과 10%의 이상치를 포함한 자료로 학습된 모형의 Nash-Sutcliffe efficiency(NSE)가 각각 0.882 및 0.858로 나타나 이상치가 모형의 성능을 저하시킬 수 있음을 확인하였다. 한편 AE를 이용하여 이상치를 다양한 비율로(5–20%) 제거한 자료로 학습된 모형의 성능을 분석한 결과 NSE가 0.883–0.896으로 이상치의 제거에 따라 모형의 성능이 Base 모형과 유사한 수준으로 개선되는 것으로 나타났다. 본 연구에서는 이상치가 딥러닝 모형에 미치는 영향을 분석하고 이상치 탐지 모형의 활용에 따른 조류 발생 예측 딥러닝 모형의 성능 향상이 가능함을 확인하였다.

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other conc

High-performance concrete(HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance(MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction perfomance.

Recently, measurement monitoring is actively used for safety management of facilities. However, since the field measurement data contains many outliers, a preprocessing process is required for reliable behavior analysis of the data. In this paper, we present a detection method of time series outliers and its applications. And we propose the precaution for the preprocessing process.