• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.78-85
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• 2012

This paper proposes an efficient algorithm to develop a 3D terrain tools which is essential in the development of 3D computer games. In particular, this paper proposes a cross detection technique to improve picking speed. In other words, this paper proposes a more efficient cross detection technique consisting of a ray and a parallelogram than a cross detection technique consisting of a ray and a triangle. So, we can confirm the faster picking speed. This paper uses a picking example among DirectX SDK samples to test it. In addition, this paper compares the number of function calls for cross detection using the existing techniques and the proposed technique. As a result, in this paper the proposed technique has fallen off to about a 50 percent than the existing techniques. And if it is calculated by times, in this paper the proposed technique was reduced to 1 to 2 seconds than the existing techniques. Additionally, in this paper 3D terrain tools are provide more improved algorithms for features such as texture splatting, height map control, object arrangement and realistic water effect. So, 3D terrain tools is available efficient in the development of real 3d computer games.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.467-470
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• 2012

This paper proposes a technique of sybil attack detection using an ID-based certificate on sensor network. First, it can solve the broadcast storm problem happening when keys are distributed to sensor nodes. Second, it prevents the replay attack by periodically generating and changing the keys of sensor nodes with Key-chain technique. Third, it authenticates sensor node's ID using hash function. So, it maximizes sensor node's memory usage, reduces communication overhead. Finally it detects Sybil attack through ID-based certificate. Therefore, the proposed technique of Sybil attack detection using ID-based certificate consider simultaneously energy efficiency and stability on sensor network environment, and can trust the provided information through sensor network.

• Journal of Photoscience
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• v.12 no.2
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• pp.87-93
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• 2005

A new assay technique to distinguish between pure compounds and the isomeric mixtures has been suggested using single molecule (SM) fluorescence detection technique. Since the number of emission spots in a fluorophorespread film prepared from a genuine dye solution was determined by experimental condition, the deviation of spot numbers from the expected values could be considered to be an indication of lower purity of the sample solution. The lower limit of sample concentration for this assay was determined to be $5{\times}10^{-10}$ M to show uniform number of expected spots within 10% uncertainties in our experimental condition. An individual fluorescence intensity distribution for a mixture of isomers having doubly different emissivities was simulated by adding distributions obtained from Cy3 and nile red (NR) independently. The result indicated that the mixture could be identified from the pure compounds through the difference in the number of Gaussian functions to fit the distribution. This new assay technique can be applied to the purity test for synthetic biofluorophores which are usually prepared in small quantities not enough for classical ensemble assays.

• Smart Structures and Systems
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• v.25 no.4
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• pp.423-432
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• 2020

In this paper, a novel but practical approach named series/parallel multi-sensing technique was proposed to evaluate the bolt looseness in a bolt group. The smart washers (SWs), which were fabricated by embedding a Lead Zirconate Titanate (PZT) transducer into two flat metal rings, were installed to the bolts group. By series connection of SWs, the impedance signals of different bolts can be obtained through only one sweep. Therefore, once the loosening occurred, the shift of different peak frequencies can be used to locate which bolt has loosened. The proposed multi input single output (MISO) damage detection scheme is very suitable for the structural health monitoring (SHM) of joint with a large number of bolts connection. Another notable contribution of this paper is the proposal of 3-dB bandwidth root mean square deviation (3 dB-RMSD) which can quantitatively evaluate the severity of bolt looseness. Compared with the traditional naked-eye observation method, the equivalent circuit based 3-dB bandwidth can accurately define the calculation range of RMSD. An experiment with three bolted connection specimens that installed the SWs was carried out to validate our proposed approach. Experimental result shows that the proposed 3 dB-RMSD based multi-sensing technique can not only identify the loosened bolt but also monitor the severity of bolt looseness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.9-14
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• 2009

In this paper, we proposed spreading code slicing technique for efficient transmitter detection in AT-DMB system with single frequency network. At the transmitter, the spreading code for transmitter identification inserted using slicing technique on forehead of null symbol and then transmitted. In this point, it requires high correlation characteristic spreading code. At the receiver, peak to peak value calculated by correlation process before signal demodulation. The transmitter information by proposed technique is employed to implement the single frequency network (SFN) which is proposed for solving a frequency inefficiency problem of the MFN. The results of the paper can be applied to wireless multimedia digital broadcasting system.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.317-321
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• 2008

Piping in the Nuclear Power plants (NPP) are mostly consisted of carbon steel pipe. The wall thinning defect is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid which flows in carbon steel pipes. This type of defect becomes the cause of damage or destruction of piping. Therefore, it is very important to measure defect which is existed not only on the welding partbut also on the whole field of pipe. Over the years, Infrared thermography (IRT) has been used as a non destructive testing methods of the various kinds of materials. This technique has many merits and applied to the industrial field but has limitation to the materials. Therefore, this method was combined with lock-in technique. So IRT detection resolution has been progressively improved using lock-in technique. In this paper, the quantitative analysis results of the location and the size of wall thinning defect that is artificially processed inside the carbon steel pipe by using IRT are obtained.

• Nuclear Engineering and Technology
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• v.33 no.2
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• pp.225-230
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• 2001

The aim of this study is to describe a usefulness of nuclear analytical technique in assessing and comparing the concentration levels through the analysis of uranium using human hair sample in the field of environment. A fission track detection technique was applied to determine the uranium concentration in human hair. Hair samples were collected from two groups of people - a) workers not dealing with uranium directly, and b) workers possibly contaminated with uranium. The concentration of $^{235}$ U for the first group varied from <1 to 39 ng/g and the second group can be estimated up to the level of $\mu$g/g. Radiographs of heavy-duty work samples contained high dense “hot spots” along a single hair. After washing in acetone and distilled water, external contamination was not totally removed. Insoluble uranium compounds were not completely washed out. The (n, f)- radiography technique, having high sensitivity, and capable of getting information on uranium content at each point of a single hair, is an excellent tool for environmental monitoring.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.212-216
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• 2011

Fault detection technique for photovoltaic power systems is significant to dramatically reduce economic damage in industrial fields. This paper presents a novel fault detection approach using Fourier neural networks and stochastic decision making strategy for photovoltaic systems. We achieve neural modeling to represent its nonlinear dynamic behaviors through a gradient descent based learning algorithm. Next, a general likelihood ratio test (GLRT) is derived for constructing a decision malling mechanism in stochastic fault detection. A testbed of photovoltaic power systems is established to conduct real-time experiments in which the DC power line communication (DPLC) technique is employed to transfer data sets measured from the photovoltaic panels to PC systems. We demonstrate our proposed fault detection methodology is reliable and practicable over this real-time experiment.