• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.45.2-45.2
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• 2016

In this study we investigate 195 spacecraft anomalies from 1998 to 2010 from Satellite News Digest (SND). We classify these data according to types of anomaly : Control, Power, Telemetry etc. We examine the association between these anomaly data and daily peak particle (electron and proton) flux data from GOES as well as their occurrence rates. To determine the association, we use two criteria that electron criterion is >10,000 pfu and proton criterion is >100 pfu. Main results from this study are as flows. First, the number of days satisfying the criteria for electron flux has a peak near a week before the anomaly day and decreases from the peak day to the anomaly day, while that for proton flux has a peak near the anomaly day. Second, we found a similar pattern for the mean daily peak particle (electron and proton) flux as a function of day before the anomaly day. Third, an examination of multiple spacecraft anomaly events, which are likely to occur by severe space weather effects, shows that anomalies mostly occur either when electron fluxes are in the declining stage, or when daily proton peak fluxes are strongly enhanced. This result is very consistent with the above statistical studies. Our results will be discussed in view of the origins of spacecraft anomaly.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of Chest Surgery
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• v.53 no.5
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• pp.243-249
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• 2020

Ebstein anomaly is a rare congenital heart malformation typically involving the tricuspid valve and the right ventricle that has a wide range of anatomical and pathophysiological presentations. Various surgical repair techniques for Ebstein anomaly have been reported because of its near-infinite anatomical variability. Cone repair for Ebstein anomaly can achieve nearly anatomical reconstruction of the tricuspid valve with promising outcomes. In this article, the surgical techniques for cone repair in adult patients with Ebstein anomaly are described in detail, and clinical experiences and technically challenging cases are presented.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.265-270
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• 2016

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

• Journal of Chest Surgery
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• v.18 no.3
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• pp.398-406
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• 1985

103 cases of open heart surgery were performed in the Department of Thoracic and Cardiovascular Surgery, Kyungpook National University Hospital in 1984. There were 90 congenital cardiac anomaly and 13 acquired heart diseases. Out of 90 congenital cardiac anomaly, 63 cases of acyanotic group and 27 cases of cyanotic group were noted. In 63 cases of acyanotic group, 11 ASD, 45 VSD and 7 other acyanotic anomaly were included. In 27 cases of cyanotic group, 4 Trilogy of Fallot, 15 TOF, 3 Pentalogy of Gasul and 5 other cyanotic anomaly were found. Among 13 cases of acquired heart diseases, 12 valvular lesions and 1 atrial myxoma were noted. Two open mitral commissurotomy and ten valve replacements were performed for 12 valve lesions. The frequent complications were acute respiratory insufficiency and low cardiac output syndrome occurred in 5 cases. The perioperative mortality was 4.8% in acyanotic congenital cardiac anomaly, 7.4% in cyanotic congenital cardiac anomaly, and 0% in acquired heart diseases. Overall mortality for 103 cases of open heart surgery was 4.9%.

• Journal of the Korean Ceramic Society
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• v.35 no.7
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• pp.665-670
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• 1998

Ferroelectric anomaly in PbO-{{{{ { {B }_{2 }O }_{3 } }}-{{{{ {TiO }_{3 } }}-BaO glasses which is observed in DAT measurements was in-vestigated together with the effect of BaO content on the shift of Cuire temperature. The temperature where the ferroelectric anomaly apperars on cooling in DTA decreased in proportion with increasing BaO content,. For as-crystallized samples the ferroelectric anomaly was not observed on heating but on cooling whilist for powder samples leached chemically from the crystallized samples both endothermic and ex-otehrmic peaks were observed. This fact suggests that the appearance of the ferroelectric anomaly in DAT largely depends on glassy phases surrounding individual {{{{ {PbTiO }_{3 } }} crystals rather than effects of grain size and crystallinity.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1392-1405
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• 2019

The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuates greatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detection algorithm based on sliding window was proposed. The algorithm combines the time series segmentation linear representation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficient than the direct detection of the original sequence. The algorithm first segments the cathode voltage time series, then calculates the length, the slope, and the mean of each line segment pattern, and maps them into a set of spatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns according to the local anomaly factor and the pattern length. The experimental results showed that the algorithm can effectively detect the abnormal range of cathode voltage.

• Journal of Korean Foot and Ankle Society
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• v.16 no.3
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• pp.148-155
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• 2012

There are many different type of congenital toe anomalies such as syndactyly, polydactyly which are more common, and less frequently macrodactyly and curly toe. Congenital anomaly of hand can decrease the hand function and easy to be visualized, so the early treatment of anomaly is natural and recommended. On the other hand, Congenital anomaly of foot rarely decrease the foot function and was hidden in the shoe, so treatment of anomaly was delayed frequently. However, the surgery can be needed, as the foot getting grown-up, discomfort of shoe fitting or intractable plantar keratosis due to secondary deformation of foot can occur. A distinct feature and surgical consideration was compared with congenital anomaly of hand and it should be taken into account in the treatment of adult toe anomalies.