• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Journal of the Korean Chemical Society
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• v.29 no.5
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• pp.533-538
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• 1985

To investigate the importance of the hydrophobic interaction in the spermine-induced collapse of DNA to a compact structure, the effect of urea on the anomalous absorbance-temperature profile of calf thymus DNA has been investigated. With the increase of the urea concentration, the trough phase of the anomalous absorbance-temperature profile was eliminated eventually. The cooperativity, enthalpy, and the midpoint of the transition to the trough region are more sensitive to urea than those of the Tm-region transition. The present data of the adverse effect of urea, a hydrophobic environmental reagent, on the thermal stabilization of the condensed state of DNA, suggest that hydrophobic interaction may play an important role in the stabilization of the tertiary structure of the collapsed state of DNA.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Journal of Korean Elementary Science Education
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• v.31 no.2
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• pp.227-242
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• 2012

This research explores the scientific reasoning of pre-service elementary teachers in experimental design. The article focuses on pre-service teachers' responses to the questions in the worksheets which involve making their knowledge claims on extinguishing of a burning candle in a closed container, evaluating anomalous data, and designing experiment to test their ideas. Their responses are interpreted in terms of categories developed by Tytler and Peterson(2003, 2004). The interrelationship between conceptual knowledge and scientific reasoning is explored using the data. It is argued that coordination of ideas and evidence must be emphasized in the scientific investigations rather than fair test.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.97-110
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• 2004

As modem web services become enormously complex, web attacks has become frequent and serious. Existing security solutions such as firewalls or signature-based intrusion detection systems are generally inadequate in securing web services, and analysis of raw web log data is simply impractical for most organizations. Visual display of "interpreted" web logs, with emphasis on anomalous web requests, is essential for an organization to efficiently track web usage patterns and detect possible web attacks. In this paper, we discuss various issues related to effective real-time visualization of web usage patterns and anomalies. We implemented a software tool named SAD (session anomaly detection) Viewer to satisfy such need and conducted an empirical study in which anomalous web traffics such as Misuse attacks, DoS attacks, Code-Red worms and Whisker scans were injected. Our study confirms that SAD Viewer is useful in assisting web security engineers to monitor web usage patterns in general and anomalous web sessions in particular.articular.

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.299-303
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• 2006

Geologic noise, especially located at shallow depth, can be a great obstacle in the interpretation of geophysical data. Thus, it is important to suppress geologic noise in order to accurately detect major anomalous bodies in the survey area. In the inversion of geophysical data, model parameters at shallow depth, which have small size and low contrast of physical property, can be regarded as one of geologic noise. The least-squares method with smoothness constraint has been widely used in the inversion of geophysical data. The method imposes a big penalty on the large model parameter, while a small penalty on the small model parameter. Therefore, it is not easy to suppress small anomalous boies. In this study, we developed a new inversion scheme which can effectively suppress geologic noise by imposing a big penalty on the slowly varying model parameter and a small penalty on the largely varying model parameter. We call the method MTE (main-target emphasizing) inversion. Applying the method to the inversion of 2.5D small loop EM data, we can ensure that it is effective in suppressing small anomalous boies and emphasizing major anomalous bodies in the survey area.

• Atmosphere
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• v.24 no.1
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• pp.77-87
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• 2014

The radar reflectivity is significantly affected by ground clutter, beam blockage, anomalous propagation (AP), birds, insects, chaff, etc. The quality of radar reflectivity is very important in quantitative precipitation estimation. Therefore, Weather Radar Center (WRC) of Korea Meteorological Administration (KMA) employed two quality control algorithms: 1) Open Radar Product Generator (ORPG) and 2) fuzzy quality control algorithm to improve quality of radar reflectivity. In this study, an occurrence of AP echoes and the performance of both quality control algorithms are investigated. Consequently, AP echoes frequently occur during the spring and fall seasons. Moreover, while the ORPG QC algorithm has the merit of removing non-precipitation echoes, such as AP echoes, it also removes weak rain echoes and snow echoes. In contrast, the fuzzy QC algorithm has the advantage of preserving snow echoes and weak rain echoes, but it eliminates the partial area of the contaminated echo, including the AP echoes.

• Geophysics and Geophysical Exploration
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• v.17 no.4
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• pp.242-246
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• 2014

Spectral induced polarization (SIP) is a useful technique, which uses electrochemical properties, for exploration of metallic sulfide minerals. Equivalent circuit analysis is commonly conducted to calculate IP parameters from SIP data. An equivalent circuit model, which indicates the SIP response of rock, has a non-uniqueness problem. For this reason, it is very important to select the proper model for accurate analysis. Thus, this study focused on suggesting a new model, which suitable for the analysis of an anomalous SIP response, such as ore. A suitability of the new model was verified by comparing it with the existing Dias model and Cole-Cole models. Analysis errors were represented as a normalized root mean square error (NRMSE). The analysis result using the Dias model was the NRMSE of 10.50% and was the NRMSE using the Cole-Cole model of 17.03%. Howerver, because the NRMSE of the new model is 0.87%, it is considered that the new model is more useful for analyzing the anomalous SIP data than other models.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.185-190
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• 2007

In this study, we developed the 2.5D EM modeling and inversion algorithm for cross-hole source and receiver geometry. Considering the cross-hole environment, we use a VMD (vertical magnetic dipole) as a source and vertical magnetic fields as a measuring data. Developed inversion algorithm is tested for the isolated block model which has a conductive and a resistivity anomaly respectively. For the conductive anomaly, its size and resistivity are inverted well on the inversion results, while for the resistive anomaly, the location of anomalous block is shown on the inverted section, but its values are far from the exact value. Furthermore, artificial conductive anomalies are shown around the resistive anomalous zone. If we consider the inversion artifact shown in the test inversion of restive block, it is almost impossible to image the resistive zone. However, the main target of EM tomography in the engineering problem is conductive target such as fault zone, and contaminated zone etc., EM tomography algorithm can be used for detecting the anomalous zone.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.1-13
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• 2020

In this paper, the author proposed a nuclear power plant (NPP) instrumentation signal faults identification algorithm. A variational autoencoder (VAE)-based model is trained by using only normal dataset as same as existing anomaly detection method, and trained model predicts which signal within the entire signal set is anomalous. Classification of anomalous signals is performed based on the reconstruction error for each kind of signal and partial derivatives of reconstruction error with respect to the specific part of an input. Simulation was conducted to acquire the data for the experiments. Through the experiments, it was identified that the proposed signal fault identification method can specify the anomalous signals within acceptable range of error.