Crossing and Conversion among North Korean Refugee-Migrants

While pivotal in the lives of North Korean refugee-migrants, the role of religion has been largely neglected in most studies. After being exposed to Protestant missionary networks, either while dwelling in Northeast China or en route to the South, about 80 percent of North Korean refugee-migrants arriving in South Korea affiliate themselves with Protestant churches. This implies that they are exposed to Protestant missionary networks either while dwelling in Northeast China or en route to the South. Some who leave South Korea for other countries or seek asylum in non-Korean societies develop their religiosity in various ways and for various reasons, as part of their aspirations, adjustment to new homes, and search for meaning. The present study aims to address this literature gap. Based on long-term ethnographic research with North Korean refugee-migrants living in South Korea, China, and Europe, the two ethnographic vignettes presented in this article represent those who are in Germany and the United Kingdom by discussing the religious encounters and conversions through which North Korean refugee-migrants make their lives and futures. It draws attention to religion as a lens through which the migrants’ negotiation of meanings, new selves and homelands, and hopes for the future can be better illuminated. The findings of this study suggest that when North Korean Christians experience religious conversion during their perilous journeys, it not only helps them to negotiate a new sense of belonging in their host societies, but it also mobilizes them to contest the existing order of things

Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors

This paper presents a detection algorithm for sensor attacks and a resilient state estimation scheme for a class of uniformly observable nonlinear systems. An adversary is supposed to corrupt a subset of sensors with the possibly unbounded signals, while the system has sensor redundancy. We design an individual high-gain observer for each measurement output so that only the observable portion of the system state is obtained. Then, a nonlinear error correcting problem is solved by collecting all the information from those partial observers and exploiting redundancy. A computationally efficient, on-line monitoring scheme is presented for attack detection. Based on the attack detection scheme, an algorithm for resilient state estimation is provided. The simulation results demonstrate the effectiveness of the proposed algorithm

Division and Unification: Seen through the Eyes of Korean Migrants in Berlin

Based on qualitative fieldwork among first generation Korean immigrants in Berlin, this article sheds light on their lived experiences of German division and unification. Our research questions are threefold; first, how do these immigrants from the divided Korea perceive the division and unification of Germany? Second, did the fact that the division of Germany could be overcome affect their views on the division and unification of the Korean Peninsula? Third, are there any differences between Koreans in Germany and Koreans in Korea with respect to their views on unification? Our research suggests that different from South Korea, where the discourses in the media and the academia tend to assume sharply antagonistic attitudes, discourses among Koreans in Germany are generally much more supportive of unification. This is because they have a positive perception of German unification in everyday life and, furthermore, have constructed for themselves a future-oriented identity as a people of the Korean Peninsula that will eventually be unified. Korean immigrants in Germany are considerably more optimistic about the possibility of Korean unification than people in South Korea

Flakeboard Thickness Swelling. Part I. Stress Relaxation in a Flakeboard Mat

The steam injection schedule best suited for dimensionally stabilizing a flake mat is one in which steam treatment is initiated before the press is closed and is continued at least until the mat attains target thickness. Experiments showed that resinless mats treated with 20 sec of steam at 600 kPa had maximum thickness swelling of 205% compared to 350% for resinless mats pressed in a conventional fashion. Reductions in thickness swelling were proportional to steam duration and pressure. Mats treated with 20 sec of steam at 1,950 kPa had only one-tenth the thickness swelling measured in conventionally pressed mats. We believe that reduction of thickness swelling is dependent on a number of factors, including plasticization of the wood, "lignin" flow, and molecular changes in the wood structure

Evaluation of automotive weatherstrip by coupled analysis of fluid-structure-noise interaction

Automotive weatherstrip plays a major role in isolating the passenger compartment from water, dust and noise, etc. Among them, the wind noise through weatherstrip is the most severe factor making the passenger uncomfortable. Weatherstrip should be in contact between the door and the body frame, and sufficient contact area is needed to minimize the wind noise through weatherstrip. But there are several factors that make it difficult to ensure sufficient contact area. First, weatherstrip rubber deteriorates as time goes by and residual stress in the rubber becomes relaxed which results in the decrease of the contact area. Second, the gap between the door and the body increases due to pressure difference at high speed. In order to predict and reduce wind noise through weatherstrip, nonlinear behaviour of rubber at high speed and he effect of rubber deformation to wind noise should both be analyzed. In the paper, rubber deformation with time is obtained by hyperelastic and viscoelastic analyses, while the gap between the door and the body frame of the vehicle going at a high speed was predicted by the coupled analysis, Fluid-Structure Interaction (FSI). And also Statistical Energy Analysis (SEA) calculates the amount of wind noise numerically caused by rubber deformation under high speed condition

Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model