• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.31-34
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• 2012

Numerical simulations of the steady and unsteady state were conducted for a coaxial swirl injector with Kerosene fuel. Non-premixed equilibrium model based on chemical equilibrium assumption was used as turbulence-chemistry interaction model. As an equations of state, SRK(Soave-Redlich-Kwong) EOS was applied to deal with the behavior of real fluid in a high pressure condition. Through the steady and unsteady computations, mean values of steady and time-averaged unsteady state were compared on the temperature and OH mass fraction and it was shown that the flame structure of steady state was different to that of time-averaged unsteady state.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.905-915
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• 2012

This paper suggests the online method to identify normal and abnormal state of water quality on the ocean USN. To define normal of the ocean water quality, we utilize the negative selection algorithm of artificial immunity system which has self and nonself identification characteristics. To distinguish abnormal status, normal state set of the ocean water quality needs to be defined. For this purpose, we generate normal state set base on mutations of each data and mutation of the data as logical product. This mutated normal (or self) sets used to identify abnormal status of the water quality. We represent the experimental result about mutated self set with the Gaussian function. Through setting the method on the ocean sensor logger, we can monitor whether the ocean water quality is normal or abnormal state by online.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2000.11a
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• pp.22-22
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• 2000

고체 추진제에서 연소실 압력이 급격히 변하는 비정상 상태에서의 연소 특성은 정상 상태와 다른 경향을 보인다. 고체 추진 시스템에서 안정적이고 필요한 성능을 얻기 위해서는 이러한 비정상 상태에서 일어나는 현상에 대한 예측이 필요하다. 고체 추진제에서 비정상 연소는 크게 두 가지 경우에 나타나게 된다. 그 중 하나는 소염을 위하여 연소실내 압력강하가 일어나는 경우이며, 다른 하나는 점화 후 압력이 상승하는 경우이다. 급격한 압력 강하로 인한 고체 추진제의 소염에 대하여 그 동안 많은 연구들이 진행되었다.(중략)

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.23-26
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• 2005

This paper describes the algorithm for deciding the status of the operating machines in the power plants. It is very important to decide whether the status of the operating machines is good or not in the industry to protect the accidents of machines and improve the operation efficiency of the plants. There are two steps to analyze the status of the running machines. First, we extract the features from the input original data. Second, we classify those features into normal/abnormal condition of the machines using the wavelet transform and the input RMS vector through the K-means algorithm. In this paper we developed the algorithm to detect the fault operation using the K-means method from the sound of the operating machines.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.7-13
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• 2020

This paper focuses on detecting abnormal patterns of respiration of humans. In this study, a contact-based device was used to acquire both normal and abnormal respiration signals. To this end, this paper reports the development of a monitoring system to investigate the respiratory status of humans in a normal environment. This work aims to classify the respiratory status, i.e., normal and abnormal status, quantitatively. The respiration signal is acquired using a contact-based medical device (BIOBPAC), and noise reduction is carried out before classifying the respiratory status. To reduce noise, a mixed filter that combines the Savitzky-Golay filter and Median filter is applied to the acquired respiration signals. The inter-class distance is maximized, and the intra-class distance is minimized. The proposed algorithm is straightforward and can be applied to a practical environment. In addition, the experimental results are provided to substantiate the proposed approach.

• Journal of the Korean Society of Propulsion Engineers
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• v.6 no.2
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• pp.75-84
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• 2002

A numerical analysis of unsteady motion in solid rocket motors with a nozzle has been conducted. The numerical formulation including modified $\kappa$-$\varepsilon$ turbulence model treats the complete conservation equation for the gas phase and the one-dimensional equations in the radial direction for the condensed phase. A fully coupled implicit scheme based on a dual time-stepping integration algorithm has been adopted to solve the governing equations. After obtaining a steady state solution, pulse and periodic oscillations of pressure are imposed at the head-end to simulate acoustic oscillations of a travelling-wave motion in the combustion chamber. Various steady and unsteady state features in the combustion chamber of a rocket motor has been analyzed as results of numerical calculations.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1233-1242
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• 2017

Various attempts have been made to prevent crime risk. One of the cases where outdoor pedestrians are attacked by criminals is the abnormal health condition. When a mental or mental condition that can not sustain normal walking due to drunkenness is exposed, the case of being a crime is revealed through crime case analysis. In this study, we propose a method for estimating the state of an individual that can be detected in outdoor activities. In order to avoid the inconvenience of installing a separate terminal for event information transmission of sensors and sensors, it is possible to estimate an abnormal state by using a 3-axis acceleration sensor built in a smart phone. The state of the user can be estimated by analyzing the momentum of the user and analyzing it with the passage of time. It is possible to distinguish the flow of time at regular intervals, to recognize the activity patterns in each time band, and to distinguish between normal and abnormal. In this study, we have evaluated the total amount of kinetic energy and kinetic energy in each direction of the acceleration sensor and the Fourier transformed value of the total energy amount to distinguish the abnormal state.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.2
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• pp.121-130
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• 2009

An unsteady numerical simulation method for an autorotating rotor in forward flight was developed. The flapping and rotational equations of motion of autorotation are continuously integrated for given time steps, meanwhile the induced velocity field at disc plane is obtained by the dynamic inflow theory embodying the unteadiness. The transitions from arbitrary initial states to equilibrium states were simulated. Steady autorotations as numerical solutions of equations were predicted by using two sources of blade airfoil data. The simulations using airfoil data which were obtained by a two dimensional Navier-Stokes solver in terms of angles of attack and Reynolds numbers have shown good agreements with wind tunnel experimental results.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.283-286
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• 2002

본 논문에서는 보다 효과적이고 강인한 워터마크 은닉을 위한 방법으로 웨이브릿 변환 영역에서 영상의 통계적 특성에 기초한 비정상상태(non-stationary)에서와 정상상태(stationary) 일반화 가우스(generalized Gaussian: GG)모델을 이용한 적응 워터마크 은닉 기술을 제안한다. 워터마크는 고주파 영역에서 연속 부대역 양자화(successive subband quantization: SSQ)를 이용하여 다해상도 영상의 웨이브릿 계수 중에서 시각적 중요 계수(perceptual significant coefficients: PSC)를 선택하여 삽입한다. 워터마크 은닉을 위한 지각 모델은 NVF(noise visibility function)함수에 의해 계산된다. 이것은 비정상상태와 정상상태의 통계적 특성을 이용하고, 국부영상 특성을 가진다. 은닉모델은 다해상도내의 각 부대역별 분산과 형상계수(shape parameter)를 사용한다. Stirmark benchmark test에 근거하여 여러 가능한 왜곡에 대한 실험에서 강인성과 비가시성에서의 우수함을 확인하였고, 비정상상태의 경우와 정상상태의 경우를 비교하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.