• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.1
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• pp.69-80
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• 2012

The garden has long served as away of thinking about nature and about culture and how each influences the other (Francis and Hester, 1990). This study, viewing the garden as a representation of the ideal, tried to seek for detailed aspects of the aforementioned ideal with the representative examples of Thomas Jefferson's gardens. Hidden behind his best known position as a politician was his other career: designer and creator of several gardens. Monticello, Academical Village, and Poplar Forest represented not only his ideals of national values like freedom, democracy and agrarian society, but also a yearning for the rural area and ideals for higher education realization. His personal desire and ideal are represented inside the spatial order, together with his ideals as a politician and the pioneer of new country. By representing the symbolic meaning metaphorically and restructuring it through a spatial scheme, Jefferson's ideal was admired and shared with visitors. In this way, Jefferson's gardens were practical stages to reveal his ideals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.216-223
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• 2019

Fault detection during production processes is one of the required operational tasks to run production processes both safely and consistently. Unexpected operational events or undetected process faults can have a serious impact on the production systems and subsequently on the final products' quality. In addition, such situations may lead to malfunctions or breakdowns of production processes. To reliably detect such abnormalities, a new one-class classification-based detection scheme has recently been developed The proposed method consists of four steps:1) noise filtering, 2) feature selection, 3) nonlinear representation and 4) outlier detection. The performance of the proposed scheme was demonstrated using the multivariate data obtained from a simulation process. The results have shown that the proposed method produced reliable monitoring results and outperforms any existing methods with an average improvement of 25.4%. The use of proper feature selection in the proposed framework yielded better detection performance.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.43-52
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• 2024

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

• Journal of the Korean Institute of Gas
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• v.27 no.1
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• pp.57-62
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• 2023

The Gas Regulator EVENT is a signal sent by the Remote Terminal Unit(RTU) installed in each local gas regulator (hereinafter referred to as "regulator"), and is an abnormal alarm that can be intuitively checked in our client server. This is an important data that enables immediate dispatch order and initial action in the situation room when a regolator abnormality occurs, and can analyze the cause of the regulator abnormality. Looking at the trend of EVENT data for regulator over the past three years, there is a clear trend of increasing unchecked EVENT data. The increase in non-checking event (actual abnormality or noise event) may mean that firstly, mechanical or pressure abnormality occurs in the actual regulator, and secondly, there is no abnormality in the actual regulator, but communication error occurred in the RTU, reset. EVENT Data may be formed as if an abnormality occurred in the static voltage due to an error, sensor error, power failure (instantaneous power failure), etc. Among them, this study analyzed the recently generated unchecked EVENT data to identify critical noise events among RTU errors, which are noise events, and reviewed ways to increase the reliability of Regulator EVENTs by reducing them.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.465-472
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• 2021

In this paper, we present an approach for detection of adverse drug reactions from drug reviews to compensate limitations of the spontaneous adverse drug reactions reporting system. Considering negative reviews usually contain adverse drug reactions, sentiment analysis on drug reviews was performed and extracted negative reviews. After then, MedDRA dictionary and named entity recognition were applied to the negative reviews to detect adverse drug reactions. For the experiment, drug reviews of Celecoxib, Naproxen, and Ibuprofen from 5 drug review sites, and analyzed. Our results showed that detection of adverse drug reactions is able to compensate to limitation of under-reporting in the spontaneous adverse drugs reactions reporting system.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.399-414
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• 2022

The purpose of this study was to test the structural model for the effect on abnormal eating behavior targeting 493 nurses. Results, The direct effects of variables affecting abnormal eating behavior were in the order of eating abstinence and socially imposed perfectionism, and these variables explained 85% of abnormal eating behavior. Explicit narcissism had a significant effect on abnormal eating behavior through socially imposed perfectionism and eating restraint, and sociocultural attitude toward appearance through eating restraint. In the multi-group moderating effect, the path coefficients between job stress and abnormal eating behavior, socially imposed perfectionism and abnormal eating behavior were different between groups. Therefore, it is necessary to find a way to lower the socially-imposed perfectionism and nursing intervention that can escape excessive eating abstinence.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.229-247
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• 2023

With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.

• Electric Engineers Magazine
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• v.257 no.1
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• pp.21-23
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• 2004

2.부하의 개폐(load switching) - 번개가 거의 모든 사람들이 인식할 수 있는 문제인 반면 Switching Surge에 의한 TVS는 대다수가 볼 수 없고 인식할 수 없는 것이다. 전력회사는 아마도 발전된 전력 공급을 위해 1개 이상의 공급원을 사용할 것이다. 자동차와 전주 혹은 나뭇가지와 전선이 충돌하는 사고가 발생할 경우 전력회사의 장비는 즉시 선로이상을 찾아 내어 선로를 OFF시키는 조치를 취한다. 전력 Feeder와 load(부하)의 결과적인 전환으로 강력한 Surge가 발생하게 된다.(중략)

• The Monthly Diabetes
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• s.289
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• pp.26-36
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• 2013

'당뇨발'은 '당뇨환자에서 신경학적 이상이나 말초혈관 이상 때문에 발생하는 발의 궤양으로 감염이나 심부 조직의 파괴를 동반하기도 하는 발의 질병'이라고 정의할 수 있다. 하지만 실제로는 발에 궤양이 발생한 상태가 아니더라도 매우 포괄적인 용어(umbrella term)로서 당뇨병환자에게서 발생하는 발의 만성 합병증을 총칭하는 것이 일반적이다. 왜냐하면 발의 궤양을 동반하지 않는 괴저(괴사), 심부 감염, 신경병증성 관절증(Charcot arthropathy) 등도 당뇨발의 범주에 포함시켜야 하기 때문이다. 이렇게 보면 '당뇨발'이란 의학적 진단명으로는 적절하지 않은 용어라 할 수도 있다. 다만 이미 일반화된 용어로서 환자의 교육과 설명에는 더 유용하다고 할 수 있다.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.563-567
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• 1998

5M 이상의 질산 매질에 있는 Ag(Ⅰ) 이온을 전착회수하기 위하여 질산 농도에 따른 전착특성을 cyclic voltammetry 방법으로 조사하였다. Ag(Ⅰ) 이온의 전착은 질산 매질의 농도에 크게 영향을 받았으며 질산 농도가 3M 이하인 경우에는 백금을 전극에서 Ag(Ⅰ) 이온이 쉽게 전착될 수 있음을 알 수 있었다. 질산농도가 5M 이상에서는 질산 자체의 환원이 활발하게 일어나 Ag(Ⅰ) 이온의 전착을 억제하였으나 용액을 혼합시킬 경우 질산 환원의 영향을 크게 감소시킬 수 있었다