• 한국정보통신학회논문지
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• 제16권1호
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• pp.33-40
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• 2012

본 논문은 생물 조기 경보 시스템을 구현하기 위한 방법을 제안한다. 이 시스템은 모니터링 데몬을 이용해 간헐적으로 데이터 사건을 생성하고, 이 데이터 집합으로부터 특징 매개변수들을 추출한다. 특징 매개변수는 6개의 변수(x/y 축 좌표, 거리, 절대 거리, 각도, 프랙털 차원)를 가지고 유도된다. 특히 프랙털 이론을 사용해 제안 알고리즘은 입력된 특징들이 독성 환경에 있는지 아닌지의 유기물 특성을 정의한다. 추출된 특징 데이터를 학습하기 위한 적절한 알고리즘을 위해 기계학습 분야에서 널리 쓰이는 확장된 학습 알고리즘(UChoo)을 사용한다. 그리고 본 알고리즘은 특징 집합들이 모니터링 데몬에 의해 주기적으로 추가된다는 BEWS의 특징을 극복하기 위해 확장된 데이터 표현 방법을 이용하는 학습 방법을 포함한다. 이 알고리즘에서 결정트리 분류기는 확장된 데이터 표현에서 가중치 매개변수를 사용하는 부류 분포 정보를 정의 한다. 실험 결과들은 제안된 BEWS가 환경적인 독성을 탐지하는데 이용 될 수 있음을 보인다.

• 한국통신학회논문지
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• 제33권8B호
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• pp.707-718
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• 2008

Traffic classification의 방법은 동적으로 변하는 application의 변화에 대처하기 위하여 페이로드나 port를 기반으로 하는 것에서 ML 알고리즘을 기반으로 하는 것으로 변하여 가고 있다. 그러나 현재의 ML 알고리즘을 이용한 traffic classification 연구는 offline 환경에 맞추어 진행되고 있다. 특히, 현재의 기존 연구들은 testing 방법으로 cross validation을 이용하여 traffic classification을 수행하고 있으며, traffic flow를 기반으로 classification 결과를 제시하고 있다. 본 논문에서는 testing방법으로 cross validation과 split validation을 이용했을 때, traffic classification의 정확도 결과를 비교한다. 또한 바이트를 기반으로 한 classification의 결과와 flow를 기반으로 한 classification의 결과를 비교해 본다. 본 논문에서는 J48, REPTree, RBFNetwork, Multilayer perceptron, BayesNet, NaiveBayes와 같은 ML 알고리즘과 다양한 feature set을 이용하여 트래픽을 분류한다. 그리고 split validation을 이용한 traffic classification에 적합한 최적의 ML 알고리즘과 feature set을 제시한다.

• Smart Structures and Systems
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• 제29권1호
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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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• 제11권5호
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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.

• 한국정밀공학회지
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• 제16권7호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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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.

• 한국공작기계학회논문집
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• 제17권1호
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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.

• 제어로봇시스템학회논문지
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• 제8권10호
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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.

• 한국정밀공학회지
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• 제16권6호
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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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• 제62권4호
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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.