• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.265-270
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• 2016

Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.

• Journal of Navigation and Port Research
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• v.35 no.9
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• pp.701-706
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• 2011

Radio waves including GPS signals, various TV communications, and radio broadcasting can be disturbed by a strong solar storm, which may occur due to solar flares and produce an ionospheric delay anomaly in the ionosphere according to the change of total electron content. Electron density irregularities can cause deep signal fading, frequently known as ionospheric scintillation, which can result in the positioning error using GPS signal. This paper proposes a detection algorithm for the ionosphere delay anomaly during a solar storm by using multi-reference stations. Different TEC grid which has irregular electron density was applied above one reference station. Then the ionospheric delay in zenith direction applied different TEC will show comparatively large ionospheric zenith delay due to the electron irregularity. The ionospheric slant delay applied an elevation angle at reference station was analyzed to detect the ionospheric delay anomaly that can result in positioning error. A simulation test was implemented and a proposed detection algorithm using data logged by four reference stations was applied to detect the ionospheric delay anomaly compared to a criterion.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.2
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• pp.279-286
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• 2013

The recent power shortages due to surge in demand for electricity highlights the importance of smart grid technologies for efficient use of power. The experimental content for vulnerability against availability of smart meter, an essential component in smart grid networks, has been reported. Designing availability protection mechanism to boost the realization possibilities of the secure smart grid is essential. In this paper, we propose a mechanism to detect the availability infringement attack for smart meter and also to find anomaly nodes through analyzing smart grid structure and traffic patterns. The proposed detection mechanism uses approximate entropy technique to decrease the detection load and increase the detection rate with few samples and utilizes the signal information(CIR or RSSI, etc.) that the anomaly node can not be changed to find the anomaly nodes. Finally simulation results of proposed method show that the detection performance and the feasibility.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.335-343
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• 2023

Drone based Magnetic Anomaly Detection measure a magnetic anomaly signal from the ferromagnetic target on the ground. We conduct a magnetic anomaly detection with 9 ferromagnetic targets on the ground. By removing the magnetic field measured in the absence of ferromagnetic targets from the experimental value, the magnetic anomaly signal is clearly measured at an altitude of 100 m. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics. The simulation results are within the GPS error range of the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1516-1529
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• 2023

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.338-345
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• 2024

Magnetic anomaly signals from the ferromagnetic targets such as ships in the sea are measured by drone-based magnetic anomaly detection. A quantum magnetometer is suspended from the drone by 4 strings. Flight altitude and speed of drone are 100 m and 5 m/s, respectively. We obtain magnetic anomaly signals of few nT from the ships clearly. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.287-294
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• 2016

APT attack aimed at the interruption of information and communication facilities and important information leakage of companies. it performs an attack using zero-day vulnerabilities, social engineering base on collected information, such as IT infra, business environment, information of employee, for a long period of time. Fragmentary response to cyber threats such as malware signature detection methods can not respond to sophisticated cyber-attacks, such as APT attacks. In this paper, we propose a cyber intrusion detection model for countermeasure of APT attack by utilizing heterogeneous system log into big-data. And it also utilizes that merging pattern-based detection methods and abnormality detection method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.58-63
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• 2010

In this paper, we propose a simple technique for the detection of a frequency jump in the GPS clock behavior. GPS satellite atomic clocks have characteristics of a second order polynomial in the long term and a non-periodic frequency drift in the short term, showing a sudden frequency jump occasionally. As satellite clock anomalies influence on GPS measurements, it requires to develop a real time technique for the detection of the clock anomaly on the real-time GPS precise point positioning. The proposed technique is based on Teager energy which is mainly used in the field of various signal processing for the detection of a specific signal or symptom. Therefore, we employed the Teager energy for the detection of the jump phenomenon of GPS satellite atomic clocks, and it showed that the proposed clock anomaly detection strategy outperforms a conventional detection methodology.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.