• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1217-1228
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• 2014

A method for faulted line detection of four parallel lines on the same tower is presented, based on four-summing and double-differential sequences of one terminal current. Four-summing and double-differential sequences of fault current can be calculated using a certain transformation matrix for parameter decoupling of four parallel transmission lines. According to fault boundary conditions, the amplitude and phase characteristics of four-summing and double-differential sequences of fault current is studied under conditions of different types of fault. Through the analysis of the relationship of terminal current and fault current, a novel methodology for fault line detection of four parallel transmission line on the same tower is put forward, which can pick out the fault lines no matter the fault occurs in single line or cross double lines. Simulation results validate that the methodology is correct and reliable under conditions of different load currents, transient resistances and fault locations.

• Journal of Navigation and Port Research
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• v.39 no.3
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• pp.179-184
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• 2015

The purpose of this study is to provide appropriate methodology to ensure the safety and integrity of the production riser in offshore structure. In order to select integrity estimation methodology for production riser, level I and II Non-destructive Damage Evaluation (NDE) methods that were applied to existing structures are classified and reviewed. Numerical analysis is performed to verify the applicability and capability on damage detection of reviewed methods. As a result, the damage detection methodology using modal strain energy is more sensitive in detection of the damage than other methods. In practice, the number of sensors is limited due to the environmental and financial conditions. The impact on damage detection performance by reducing the number of sensors is systematically investigated through a series of numerical analyses and the results are discussed. The optimal number of sensor for the integrity estimation of production riser is recommended.

• Structural Engineering and Mechanics
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• v.39 no.2
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• pp.223-239
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• 2011

This paper presents a methodology to detect and locate damages and faults in orthotropic plate structures. A specific damage index based on dynamic mode shapes of the damaged and undamaged structures has been introduced. The governing differential equation on transverse deformation, the transverse shear force equations and the invariant expression for the sum of transverse loading of an orthotropic plate are employed to obtain the aforementioned damage indices. The validity of the proposed methodology for isotropic and orthotropic damage states is demonstrated using a numerical example. It is shown that the algorithm is able to detect damages for both isotropic and orthotropic damage states acceptably.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.10
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• pp.1065-1071
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• 2005

Many techniques of leak detection in pipeline systems have developed based on the propagation wave speeds and wave attenuation. In this paper, the transient analysis methodology is used for calculating the wave speed in the plastic pipe and a frequency analysis methodology is developed for leakage detection in water pipe networks. Data acquisition system for pressurized pipeline system were designed md fabricated to obtain high frequency pressure data. The methodology properly handles the unavoidable uncertainties in measurement and modeling error. Based on information from head pressure test data, it provides leak prediction capability from the transient events with leakage.

• 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.17 no.6
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• pp.859-880
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• 2016

This study aims to establish an effective methodology for the detection of instant damages occurred in cable-stayed bridges with the measurements of cable vibration and structural temperatures. A transfer coefficient for the daily temperature variation and another for the long-term temperature variation are firstly determined to eliminate the environmental temperature effects from the cable force variation. Several thresholds corresponding to different levels of exceedance probability are then obtained to decide four upper criteria and four lower criteria for damage detection. With these criteria, the monitoring data for three stay cables of Ai-Lan Bridge are analyzed and compared to verify the proposed damage detection methodology. The simulated results to consider various damage scenarios unambiguously indicate that the damages with cable force changes larger than ${\pm}1%$ can be confidently detected. As for the required time to detect damage, it is found that the cases with ${\pm}2%$ of cable force change can be discovered in no more than 6 hours and those with ${\pm}1.5%$ of cable force change can be identified in at most 9 hours. This methodology is also investigated for more lightly monitored cases where only the air temperature measurement is available. Under such circumstances, the damages with cable force changes larger than ${\pm}1.5%$ can be detected within 12 hours. Even though not exhaustively reflecting the environmental temperature effects on the cable force variation, both the effective temperature and the air temperature can be considered as valid indices to eliminate these effects at high and low monitoring costs.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1042-1044
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• 2003

A methodology of the integrating the similar color circle search of the spectral data and segmentation of the height data is developed. The method is then applied to study areas, and the results by IKONOS, LIDAR and data fusion are verified with the ground truth, and examined in terms of the accuracy. Results show that with the data fusion the accuracy are improved by about 15% in most of the study areas. The methodology for the detection of individual tree stands by data fusion is explored, and the utility of combinatorial use of the spectral and the height information is demonstrated.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.85-90
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• 1996

In order to detect and suppress chatter in turning processes a stability control methodology was studied through manipulation of spindle speeds regarding to chatter frequencies. The chatter frequency was identified by monitoring and signal processing of sound pressure during turning on a lathe. The stability control methodology can select stable spindle speeds without knowing a prior knowledge of machine compliances and cutting dynamics. Teliability of the developed stability control methodology was verified through turning experiments on an engine lathe. Experimental results show that a microphone is an excellent sensor for chatter detection and control

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.41-59
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• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.