• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.215-222
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• 1995

The objective of this paper is to develop a finite element structural analysis program using VB(Visual Basic) which has been recently getting popular as development tools of application program for Windows. VB provides several functions to develop an application program easily by supporting event-driven programming method and graphic object as control data type. This system is an integrated processor including preprocessor, solver and postprocessor. Automatic mesh generation is available at preprocess stage, and graphic presentation of deformation and stress is also represented at postprocess one.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1103-1111
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• 2015

This paper presents a new prediction method based on relative error incorporated with a penalized regression. The proposed method consists of fully data-driven procedures that is fast, simple, and easy to implement. An example of real data analysis and some simulation results were given to prove that the proposed approach works in practice.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.37-38
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• 2018

BIM(Building Information Modeling) in the architecture, VDC(Virtual Design and Construction) defined CIFE(Center for Integrated Facility Engineering) of Stanford university in USA, and Data-driven design definition issued by TECDOC-1284 of IAEA are doing data-level design generated by 3D CAD technology, integrating and managing related information based on the 3D model, and Using 3D models effectively during nuclear power plant life cycle. 3D model of domestic nuclear power industry is using interference review between design fields, 4D system linked 3D construction model and schedule activity, but the 3D model generated in the design phase is effectively not utilized during the construction, operation, decommissioning. therefore, This study is aimed to suggest 3D model LOD(Level of Detail) advancement method through the analysis of existing literature, 2D drawings, and 3D models throughout nuclear power plant lifecycle.

• Smart Structures and Systems
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• v.31 no.2
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• pp.183-197
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• 2023

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.

• The korean journal of orthodontics
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• v.41 no.5
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• pp.354-360
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• 2011

Objective: To investigate the effects of different pilot-drilling methods on the biomechanical stability of self-tapping mini-implant systems at the time of placement in and removal from artificial bone blocks. Methods: Two types of artificial bone blocks (2-mm and 4-mm, 102-pounds per cubic foot [102-PCF] polyurethane foam layered over 100-mm, 40-PCF polyurethane foam) were custom-fabricated. Eight mini-implants were placed using the conventional motor-driven pilot-drilling method and another 8 mini-implants were placed using a novel manual pilot-drilling method (using a manual drill) within each of the 2-mm and 4-mm layered blocks. The maximum torque values at insertion and removal of the mini-implants were measured, and the total energy was calculated. The data were statistically analyzed using linear regression analysis. Results: The maximum insertion torque was similar regardless of block thickness or pilot-drilling method. Regardless of the pilot-drilling method, the maximum removal torque for the 4-mm block was statistically higher than that for the 2-mm block. For a given block, the total energy at both insertion and removal of the mini-implant for the manual pilot-drilling method were statistically higher than those for the motor-driven pilot-drilling method. Further, the total energies at removal for the 2-mm block was higher than that for the 4-mm block, but the energies at insertion were not influenced by the type of bone blocks. Conclusions: During the insertion and removal of mini-implants in artificial bone blocks, the effect of the manual pilot-drilling method on energy usage was similar to that of the conventional, motor-driven pilot-drilling method.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.143-151
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• 2012

A new data-driven method for the design of a blind modulation frequency filter that suppresses the slow-varying noise components is proposed. The proposed method is based on the temporal local decorrelation of the feature vector sequence, and is done on an utterance-by-utterance basis. Although the conventional modulation frequency filtering approaches the same form regardless of the task and environment conditions, the proposed method can provide an adaptive modulation frequency filter that outperforms conventional methods for each utterance. In addition, the method ultimately performs channel normalization in a feature domain with applications to log-spectral parameters. The performance was evaluated by speaker-independent isolated-word recognition experiments under additive noise environments. The proposed method achieved outstanding improvement for speech recognition in environments with significant noise and was also effective in a range of feature representations.

• ETRI Journal
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• v.29 no.3
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• pp.300-304
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• 2007

This paper proposes a new data-driven method for high-pass approaches, which suppresses slow-varying noise components. Conventional high-pass approaches are based on the idea of decorrelating the feature vector sequence, and are trying for adaptability to various conditions. The proposed method is based on temporal local decorrelation using the information-maximization theory for each utterance. This is performed on an utterance-by-utterance basis, which provides an adaptive channel normalization filter for each condition. The performance of the proposed method is evaluated by isolated-word recognition experiments with channel distortion. Experimental results show that the proposed method yields outstanding improvement for channel-distorted speech recognition.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.227-229
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• 2010

This study presents an application of the 3D patch extraction method which is based on gradient-driven properties to obtain 3D planar patches over the terrain and man-made objects from lidar data. The method which was exploited in this study is composed of a sequence of processes: segmentation by slope, initiation of triggering patches by mode selection, and expansion of the triggering patches. Since urban areas contain many planar regions over the terrain surface, application of the method has been experimented to extract 3D planar patches not only from non-terrain objects but also from the terrain. The experimental result shows that the method is efficient to acquire 3D planar patches.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.