• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.33-40
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• 2012

This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.707-718
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• 2008

The methodology of classifying traffics is changing from payload based or port based to machine learning based in order to overcome the dynamic changes of application's characteristics. However, current state of traffic classification using machine learning (ML) algorithms is ongoing under the offline environment. Specifically, most of the current works provide results of traffic classification using cross validation as a test method. Also, they show classification results based on traffic flows. However, these traffic classification results are not useful for practical environments of the network traffic monitoring. This paper compares the classification results using cross validation with those of using split validation as the test method. Also, this paper compares the classification results based on flow to those based on bytes. We classify network traffics by using various feature sets and machine learning algorithms such as J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, and NaiveBayes. In this paper, we find the best feature sets and the best ML algorithm for classifying traffics using the split validation.

• 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.

• Smart Structures and Systems
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• v.11 no.5
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• pp.497-510
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• 2013

Quick, accurate damage monitoring is strongly required for damage assessment in the aftermath of a large natural disaster. Wireless sensor networks are promising technologies to acquire damage information in a citywide area. The wireless sensor networks, however, would be faced with difficulty to collect data in real-time and to expand the scalability of the networks. This paper discusses a scheme of network architecture to cove a whole city in multi-tier heterogeneous networks, which consist of wireless sensor networks, access networks and a backbone network. We first review previous studies for citywide damage monitoring, and then discuss the feature of multi-tier heterogeneous networks to cover a citywide area.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.101-108
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• 1999

A reliable and practical monitoring of drill breakage is a crucial technique in automatic machining system. In this study, a real-time monitoring system was developed to predict drill breakage using both spindle and z-axis motor current. Drill breakage is monitored by detecting the level of residual motor current which is obtained through the moving average filter algorithm. The residual exhibits a feature of sharp decrease just before drill breakage. Therefore, drill breakage can be predicted by detecting this characteristic of residual component. Z-axis motor current is better to predict the drill breakage than spindle motor current, because the former is faster in response than the latter when drill breakage is occurred. The evaluation experiments have shown that the developed monitoring system works very well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.148-152
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• 2000

Detection of tool wear is very important in automated manufacturing. This paper presents tool condition monitoring system based on the wavelet analysis of the AC servo motro current in drilling and milling process. The current measurement system is relatively simple and its mounting will not affect machining operations. The discrete wavelet transform was used to decompose the current signal of a spindle AC servo motor in time - frequency domain. The feature vectors were extracted from the decomposed signals and compared for normal and wear condition. The results show the possibility for the effective application of wavelet analysis to tool condition monitoring.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.98-105
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• 2008

In this paper, the system for monitoring process of aluminum laser welding was developed using the light signal emitted from the plasma which comes from interaction between material and laser. Photodiode for monitoring system was selected based on the spectrum analysis of light from plasma and keyhole. Behavior of plasma and keyhole was analyzed through the sensor signals. Value of sensor signal represented the light intensity and fluctuation of signal indicated the stability of plasma and keyhole. For the relation between welding condition and sensor signals, the input power and weld geometry greatly effected on the average of each sensor signals. Using the feature values of signals, estimation model for tensile strength of weld was formulated with neural network algorithm. Performance of this model was verified through coefficient of determination and average error rate.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.367-375
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• 2019

Hemifacial spasm (HFS) is due to the vascular compression of the facial nerve at its root exit zone (REZ). Microvascular decompression (MVD) of the facial nerve near the REZ is an effective treatment for HFS. In MVD for HFS, intraoperative neurophysiological monitoring (INM) has two purposes. The first purpose is to prevent injury to neural structures such as the vestibulocochlear nerve and facial nerve during MVD surgery, which is possible through INM of brainstem auditory evoked potential and facial nerve electromyography (EMG). The second purpose is the unique feature of MVD for HFS, which is to assess and optimize the effectiveness of the vascular decompression. The purpose is achieved mainly through monitoring of abnormal facial nerve EMG that is called as lateral spread response (LSR) and is also partially possible through Z-L response, facial F-wave, and facial motor evoked potentials. Based on the information regarding INM mentioned above, MVD for HFS can be considered as a more safe and effective treatment.