The high-level radioactive waste repository must ensure its performance for a long period of time enough to sufficiently reduce the potential risk of the waste, and for this purpose, multibarrier systems consisting of engineered and natural barrier systems are applied. If waste nuclides leak, the dominating mechanisms facilitating their movement toward human habitats include advection, dispersion and diffusion along groundwater flows. Therefore, it is of great importance to accurately assess the hydrogeological and geochemical characteristics of the host rock because it acts as a flow medium. Normally, borehole investigations were used to evaluate the characteristics and the use of multi-packer system is more efficient and economical compared to standpipes, as it divides a single borehole into multiple sections by installing multiple packers. For effective analyses and groundwater sampling, the entire system is designed by preselecting sections where groundwater flow is clearly remarkable. The selection is based on the analyses of various borehole and rock core logging data. Generally, sections with a high frequency of joints and evident water flow are chosen. Analyzing the logging data, which can be considered continuous, gives several local points where the results exhibit significant local changes. These clear deviations can be considered outliers within the data set, and machine learning algorithms have been frequently applied to classify them. The algorithms applied in this study include DBSCAN (density based spatial clustering of application with noise), OCSVM (one class support vector method), KNN (K nearest neighbor), and isolation forest, of which are widely used in many applications. This paper aims to evaluate the applicability of the aforementioned four algorithms to the design of multi-packer system. The data used for this evaluation were obtained from DB-2 borehole logging data, which is a deep borehole locates near KURT.

Regarding high-dimensional heterogeneous data, combined with the existing algorithms' poor mining accuracy and parameter sensitivity, this paper proposes a local outlier mining algorithm based on neighborhood density. Use region segmentation to split high-dimensional data into reasonable sub-regions, reducing the difficulty of processing a large amount of high-dimensional data. The kernel neighborhood density is used to replace the average neighborhood density, so that the density calculation has nothing to do with data heterogeneity. Finally, the neighborhood state and outlier state of the data are further determined on the basis of neighborhood density to improve the accuracy of outlier mining. Through artificial and UCI data set simulation results, it shows that data volume and data dimension are the main factors that affect data outlier mining. The accuracy, coverage, and efficiency of the algorithm proposed in this paper are significantly better than those of the comparison algorithm, and it has better adaptability to different types of data sets.

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.

본 논문은 정치적으로나 문화적으로 영국과 유럽의 변방에 있는 국외자적 지위에 항시 민감한 작가 3명의 소설을 탐구한다. 에드나 오브라이언의『작은 붉은 의자들』, 조 베이커의『시골길, 나무 한 그루』및 세바스찬 배리의『한시적 신사』, 등을 읽는다. 우선『작은 붉은 의자들』에서 오브라이언은 아일랜드의 유럽변방의식을 주제로 비영감적으로 가능한 플롯을 만드는데, 최근 유럽역사상 가장 악명높은 불법자 중 하나(보스니아의 학살자 라반 카라치크) 같은 주인공은 아일랜드의 외딴 곳 코나트 지역에 잠시 숨어서 비교적 잘 지낸다. 베이커의『시골길, 나무 한 그루』에서는, 반면에, 아일랜드시민 도망자에게 일종의 피난처가 되는 것은 유럽이 된다. 내가 “일종”이라고 한 것은, 피난하기 위해서 소설의 문화적 도피자—극작가며 소설가인 새뮤엘 베겟—는 어정쩡하게, 아니면 위험하게 2차대전 중에 프랑스에 머물기로 한다. 마지막으로, 배리의 포스트콜로니얼 아일랜드의 세기 중엽의 “웨스트 브릿트” 소설인『한시적 신사』의 텍스트인 공상적 메모와는 우리에게 아일랜드적이라는 것은 전적으로 거의 다루어 지지 않았다는 것을 상기시킨다. 이 두 가지 요구는 아일랜드 사람이라는 것을 요구하고 동시에 유럽인이며 영국인임도 요구한다.

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.

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

UWB (Ultra Wide Band) refers to a system with a bandwidth of over 500 MHz or a bandwidth of 20% of the center frequency. It is robust against channel fading and has a wide signal bandwidth. Using the IR-UWB based ranging system, it is possible to obtain decimeter-level ranging accuracy. Furthermore, IR-UWB system enables acquisition over glass or cement with high resolution. In recent years, IR-UWB-based ranging chipsets have become cheap and popular, and it has become possible to implement positioning systems of several tens of centimeters. The system can be configured as one-way ranging (OWR) positioning system for fast ranging and TWR (two-way ranging) positioning system for cheap and robust ranging. On the other hand, the ranging based positioning system has a limitation on the number of terminals for localization because it takes time to perform a communication procedure to perform ranging. To overcome this problem, code multiplexing and channel multiplexing are performed. However, errors occur in measurement due to interference between channels and code, multipath, and so on. The measurement filtering is used to reduce the measurement error, but more fundamentally, techniques for removing these measurements should be studied. First, the TWR based positioning was analyzed from a stochastic point of view and the effects of outlier measurements were summarized. The positioning algorithm for analytically identifying and removing single outlier is summarized and extended to three dimensions. Through the simulation, we have verified the algorithm to detect and remove single outliers.