• Journal of Environmental Science International
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• v.4 no.1
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• pp.91-103
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• 1995

The reaction rate, equilibrium, and flow injection analysis methods were fundamentally evaluated for the determination of aqueous ammonia. The selected indophenol blue method was based on the formation of indophenol blue in which ammonium ion reacted with hypochlorite and phenol in alkaline solution. In the optimized reaction condition, the reaction followed 1st order reaction kinetics and the final product was stable. The absorbance measurements before and after the equilibrium were utilized for the reaction rate and equilibrium methods. The reaction rate methods, based on the relative analytical signals for the possibility of eliminating interferents, were shown to have good linear calibration curves but the detection limit and the calibration sensitivity were poorer than those in the equilibrium method. The detection limits were 32-49 pub and 24 pub for the reaction rate and equilibrium methods, respectively In the flow injection analysis, the absorbance was measured before the equilibrium reached and thus resulted in 30% reduction of calibration sensitivity. However, the detection limit was 11 ppb, indicating that the peak-to-peak noise for the blank was remarkably improved. Compared to the manual methods, the optimized experimental condition in a closed reaction system reduced the blank absorbance and the inclusion of ammonia from the atmosphere was prevented. In addition, highly reproducible mixing of sample and reagents and analytical data extracted from continuous recording showed excellent reproducibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.893-910
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• 2017

In this paper, we propose a simple online multiple object (human) tracking method, LKDeep (Lucas-Kanade feature and Detection based Simple Online Multiple Object Tracker), which can run in fast online enough on CPU core only with acceptable tracking performance for embedded surveillance purpose. The proposed LKDeep is a pragmatic hybrid approach which tracks multiple objects (humans) mainly based on LK features but is compensated by detection on periodic times or on necessity times. Compared to other state-of-the-art multiple object tracking methods based on 'Tracking-By-Detection (TBD)' approach, the proposed LKDeep is faster since it does not have to detect object on every frame and it utilizes simple association rule, but it shows a good object tracking performance. Through experiments in comparison with other multiple object tracking (MOT) methods using the public DPM detector among online state-of-the-art MOT methods reported in MOT challenge [1], it is shown that the proposed simple online MOT method, LKDeep runs faster but with good tracking performance for surveillance purpose. It is further observed through single object tracking (SOT) visual tracker benchmark experiment [2] that LKDeep with an optimized deep learning detector can run in online fast with comparable tracking performance to other state-of-the-art SOT methods.

• Journal of the Korean earth science society
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• v.36 no.5
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• pp.437-446
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• 2015

Many edge detection methods, based on horizontal and vertical derivatives, have been introduced to provide us with intuitive information about the horizontal distribution of a subsurface anomalous body. Understanding the characteristics of each edge detection method is important for selecting an optimized method. In order to compare the characteristics of the individual methods, this study applied each method to synthetic magnetic data created using homogeneous prisms with different sizes, the numbers of bodies, and spacings between them. Seven edge detection methods were comprehensively and quantitatively analyzed: the total horizontal derivative (HD), the vertical derivative (VD), the 3D analytic signal (AS), the title derivative (TD), the theta map (TM), the horizontal derivative of tilt angle (HTD), and the normalized total horizontal derivative (NHD). HD and VD showed average good performance for a single-body model, but failed to detect multiple bodies. AS traced the edge for a single-body model comparatively well, but it was unable to detect an angulated corner and multiple bodies at the same time. TD and TM performed well in delineating the edges of shallower and larger bodies, but they showed relatively poor performance for deeper and smaller bodies. In contrast, they had a significant advantage in detecting the edges of multiple bodies. HTD showed poor performance in tracing close bodies since it was sensitive to an interference effect. NHD showed great performance under an appropriate window.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.929-931
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• 2015

As the digital image processing technology develops, the edge in the image is widely utilized in various fields such as the object recognition and detection. Most of the current methods to detect the edge use the fixed weighting mask of Sobel or Roberts. Such current methods have an advantage that the implementation is simple but have a disadvantage that the characteristics of the edge detection are more or less insufficient. Thus, an algorithm using the modified morphology is proposed in order to supplement such problems of the current edge detection methods and obtain the excellent edge detection, and also a simulation using this algorithm is conducted to compare with such current methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.277-285
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• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.15-20
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• 2016

Compared to the MIMO system using the spatial multiplexing methods and the MIMO system using the diversity scheme achieved a high rate, but the lower the diversity gain to improve the data transmission reliability should separate the spatial stream at the MIMO receiver. In this paper, we compared Channel capacity detection methods with the Lattice code, the 3-user interference channel and linear channel interference detection methods ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) detection methods. The channel is a Diagonal channel. In other words, Diagonal channel is confirmed by the inverse matrix satisfies the properties of Jacket are element-wise inverse to $[H]_N[H]_N^{-1}=[I]_N$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.900-902
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• 2015

In the modern society, according to the advancement in digital image processing technology, edge detection is being utilized in various application sectors such as smart device and medical, etc. In existing edge detection methods, there are Sobel, Prewitt, Roberts and Laplacian, etc, which uses the mask. These previous methods are easy to implement but shows somewhat insufficient results. Therefore, in order to compensate the problems of existing methods, in this paper, an algorithm that detects the edge using the local mask and morphological operation was proposed and the detection performance was compared against the previous methods.

• Convergence Security Journal
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• v.18 no.4
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• pp.11-17
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• 2018

Hardware Keyloggers are available in a variety of modular keylogger products with small size and Wi-Fi communication capabilities that can be concealed inside the keyboard. Such keyloggers are more likely to leak important information and sensitive information from government, military, business and individuals because they are difficult to detect if they are used by a third party for malicious purposes. However, unlike software keyloggers, research on security solutions and detection methods are relatively small in number. This paper, we investigate security vulnerability caused by hardware keylogger and existing detection methods, and improve the detection possibility of modular hardware keylogger through non-destructive measurement methods, such as power consumption of keyboard, infrared temperature, and X-ray. Furthenmore, We propose a method that can be done with experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.