• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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• pp.1618-1631
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• 1994

In this paper, in-process chatter detection methodology which utilizes nondimensional characteristic variables is introduced. To obtain nondimensional chatter detection indexes which are constant regardless of the cutting conditions during machining with the same tool and workpiece material, both the cutting forces and accelerations are measured and processed in time and frequency domain. The indexes are calculated from the present and past value of the acceleration and cutting force signals in time and frequency domain. The chatter is identified when these chatter detection indexes are bigger than the threshold which is decided by preliminary experiments. The experiment shows that these indexes works very well in-process chatter detection.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.399-408
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• 2012

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.357-367
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• 2012

This paper presents a two-stage methodology for anomaly detection from hyperspectral imagery that consists of transform-based feature extraction and selection, and computation of a local spatial auto-correlation statistic. First, principal component transform and 3D wavelet transform are applied to reduce redundant spectral information from hyperspectral imagery. Then feature selection based on global skewness and the portion of highly skewed sub-areas is followed to find optimal features for anomaly detection. Finally, a local indicator of spatial association (LISA) statistic is computed to account for both spectral and spatial information unlike traditional anomaly detection methodology based only on spectral information. An experiment using airborne CASI imagery is carried out to illustrate the applicability of the proposed anomaly detection methodology. From the experiments, anomaly detection based on the LISA statistic linked with the selection of optimal features outperformed both the traditional RX detector which uses only spectral information, and the case using major principal components with large eigen-values. The combination of low- and high-frequency components by 3D wavelet transform showed the best detection capability, compared with the case using optimal features selected from principal components.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.545-559
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• 2010

This paper presents the results of a pilot study and verification of a concept of a novel methodology for damage detection and assessment of water distribution system. The unique feature of the proposed noninvasive methodology is the use of accelerometers installed on the pipe surface, instead of pressure sensors that are traditionally installed invasively. Experimental observations show that a sharp change in pressure is always accompanied by a sharp change of pipe surface acceleration at the corresponding locations along the pipe length. Therefore, water pressure-monitoring can be transformed into acceleration-monitoring of the pipe surface. The latter is a significantly more economical alternative due to the use of less expensive sensors such as MEMS (Micro-Electro-Mechanical Systems) or other acceleration sensors. In this scenario, monitoring is made for Maximum Pipe Acceleration Gradient (MPAG) rather than Maximum Water Head Gradient (MWHG). This paper presents the results of a small-scale laboratory experiment that serves as the proof of concept of the proposed technology. The ultimate goal of this study is to improve upon the existing SCADA (Supervisory Control And Data Acquisition) by integrating the proposed non-invasive monitoring techniques to ultimately develop the next generation SCADA system for water distribution systems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.167-174
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• 2006

In this paper, the applicability of strain data to a strain-energy-based damage evaluation methodology in detecting damage in a beam-like structure is demonstrated. For the purpose of this study, one of the premier damage evaluation methodology based on modal amplitudes, the damage index method, is expanded to accomodate strain data, and the numerical and experimental verifications are conducted using numerical and experimental data. To compare the relative performance of damage detection, the damage evaluation using acceleration data is also performed for the same damage scenarios. The experimental strain and acceleration data are extracted from laboratory static and dynamic tests. The numerical and experimental studies show that the strain data as well as acceleration data can be utilized in detecting damage.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.17-25
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• 2009

This paper presents a methodology for a fall detection using the feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. Nineteen number of wavelet transformed coefficients captured by a triaxial accelerometer are selected as minimized features using the non-overlap area distribution measurement method. The proposed methodology shows that sensitivity, specificity, and accuracy are 95%, 97.25%, and 96.125%, respectively.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• Smart Structures and Systems
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• v.5 no.4
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• pp.357-367
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• 2009

Structural Health Monitoring (SHM), particularly remote monitoring, is an emerging field with great potential to help infrastructure owners obtain more and up-to-date knowledge of their structures. The methodology could provide supplemental information to guide the frequency and extent of visual inspections, and the possible need for maintenance. The instrumentation for a SHM system needs to be developed with longevity and the objectives for the system in mind. Sensors need to be selected for reliability and durability, sited where they provide the maximum information for the objectives, and where they can be accessed and replaced should the need arise over the monitoring period. With the rapid changes now occurring with sensors and software, flexibility needs to be in place to allow the system to be upgraded over time. Damage detection needs to be considered in terms of the type of damage that needs to be detected, informing maintenance requirements, and how detection can be achieved. Current vibration analysis techniques appear not yet to have achieved the necessary sensitivity for that purpose. Societal factors will influence the design of a SHM system in terms of the sophistication of the instrumentation and methodology employed.