• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.558-561
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• 2004

Currently, most IP multicasting applications are implemented based on Any-Source Multicast (ASM) model that supports many to many multicast services. However, it is known that current ASM-based multicast architecture has several deployment problems such as address allocation, lack of access control, and inefficient handling of well-known multicast sources. Source-Specific Multicast (SSM) working group in IETF proposed SSM architecture to overcome the weaknesses of ASM architecture. The architecture of SSM is based on one to many multicast services. Also, in order to provide SSM service, Multicast Listener Discovery Version 2 (MLDv2) protocol should be supported. In this paper, we introduce the architecture of SSM protocol and multicast group management protocol. After that, we present the architecture and implementation of IPv6 SSM and MLDv2 protocols in Linux systems.

• Journal of Ocean Engineering and Technology
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• v.35 no.3
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• pp.238-246
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• 2021

The survivability of a naval ship is the ability of the ship and its onboard systems to remain functional and continue a designated mission in man-made hostile environments. A passive decoy system is primarily used as a weapon system for improving the survivability of a naval ship. In this study, an engagement scenario-based simulation program was developed for decoy effectiveness assessments against an anti-ship missile (ASM), which tracks a target with sea-skimming and active radar homing. The program can explain the characteristics of a target ship, such as the radar cross section and evasive maneuvers, as well as the operational performance of the onboard decoy system, the guidance method of the ASM, and the engagement environment's wind speed and direction. This paper describes the theory and formulations, configuration, and user interface of the developed program. Numerical examples of a decoy effect assessment of a virtual naval ship against an ASM are presented.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.1
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• pp.58-72
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• 2009

In this study, the organic matter and biomass was characterized by using respirometry based on ASM No.2d (Activated Sludge Model No.2d). The activated sludge models are based on the ASM No.2d model, published by the IAWQ(International Association on Water Quality) task group on mathematical modeling for design and operation of biological wastewater treatment processes. For this study, OUR(Oxygen Uptake Rate) measurements were made on filtered as well as non-filtered wastewater. Also, GC-FID and LC analysis were applied for the estimation of VFAs(Volatile Fatty Acids) COD(S_A) in slowly bio-degradable soluble substrates of the ASM No.2d. Therefore, this study was intended to clearly identify slowly bio-degradable dissolved materials(S_S) and particulate materials(X_I). In addition, a method capable of determining the accurate time to measure non-biodegradable COD(S_I), by the change of transition graphs in the process of measuring microbial OUR, was presented in this study. Influent fractionation is a critical step in the model calibrations. From the results of respirometry on filtered wastewater, the fraction of fermentable and readily biodegradable organic matter(S_F), fermentation products(S_A), inert soluble matter(S_I), slowly biodegradable matter(X_S) and inert particular matter(X_I) was 33.2%, 14.1%, 6.9%, 34.7%, 5.8%, respectively. The active heterotrophic biomass fraction(X_H) was about 5.3%.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.383-396
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• 2009

Bone age assessment has been widely used in pediatrics to identify endocrine problems of children. Since the number of trained doctors is far less than the demands, there has been numerous requests for automatic estimation of bone age. Therefore, in this paper, we propose an automatic bone age assessment method that utilizes pattern classification techniques. The proposed method consists of three modules; a finger segmentation module, a normalized shape model generation module and a bone age estimation module. The finger segmentation module segments fingers and epiphyseal regions by means of various image processing algorithms. The shape model abstraction module employ ASM to improves the accuracy of feature extraction for bone age estimation. In addition, SVM is used for estimation of bone age. Features for the estimation include the length of bone and the ratios of bone length. We evaluated the performance of the proposed method through statistical analysis by comparing the bone age assessment results by clinical experts and the proposed automatic method. Through the experimental results, the mean error of the assessment was 0.679 year, which was better than the average error acceptable in clinical practice.

• Clean Technology
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• v.28 no.1
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• pp.63-78
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• 2022

Electronics industrial wastewater treatment facilities release organic wastewaters containing high concentrations of organic pollutants and more than 20 toxic non-biodegradable pollutants. One of the major challenges of the fourth industrial revolution era for the electronics industry is how to treat electronics industrial wastewater efficiently. Therefore, it is necessary to develop an electronics industrial wastewater modeling technique that can evaluate the removal efficiency of organic pollutants, such as chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), and tetramethylammonium hydroxide (TMAH), by digital twinning an electronics industrial organic wastewater treatment facility in a cyber physical system (CPS). In this study, an electronics industrial wastewater activated sludge model (e-ASM) was developed based on the theoretical reaction rates for the removal mechanisms of electronics industrial wastewater considering the growth and decay of micro-organisms. The developed e-ASM can model complex biological removal mechanisms, such as the inhibition of nitrification micro-organisms by non-biodegradable organic pollutants including TMAH, as well as the oxidation, nitrification, and denitrification processes. The proposed e-ASM can be implemented as a Water Digital Twin for real electronics industrial wastewater treatment systems and be utilized for process modeling, effluent quality prediction, process selection, and design efficiency across varying influent characteristics on a CPS.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.343-345
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• 2006

본 논문에서는 ASM(Active Shape Model)과 상태 기반 모델을 사용하여 동영상으로부터 얼굴 표정을 인식하는 방법을 제안한다. ASM을 이용하여 하나의 입력영상에 대한 얼굴요소 특징점들을 정합하고 그 과정에서 생성되는 모양 파라미터 벡터를 추출한다. 동영상에 대해 추출되는 모양 파라미터 벡터 집합을 세 가지상태 중 한 가지를 가지는 상태 벡터로 변환하고 분류기를 통해 얼굴의 표정을 인식한다. 분류단계에서는 분류성능을 높이기 위해 새로운 개체 기반 학습 방법을 제안한다. 실험에서는 새로이 제안한 개체 기반 학습 방법이 KNN 분류기보다 더 좋은 인식률을 나타내는 것을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.868-870
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• 2005

본 논문은 포즈가 취해진 얼굴의 정확한 특징점 추출을 위하여 적응적인 평균 모양 방법을 이용한 ASM(Active Shape Model)을 제안한다. ASM은 사람 얼굴의 모양을 모델링하기 위하여 통계학상의 모양 모델을 이용한다. 통계학상의 모양 모델의 평균 모양은 입력 영상의 얼굴 포즈와 관계없이 하나로 고정되어 있으며, 이는 모양 모델 제한 조건 검사 및 복원과정에서 잘못된 결과를 만드는 원인이 된다. 이러한 문제를 해결하기 위하여 입력 영상의 얼굴 모양에 적응적인 평균 모양을 제안하며, 실험을 통해 제안한 방법이 고정된 평균 모양 방법의 문제를 해결하고 특징점 추출 성능을 향상시킴을 보였다.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.5
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• pp.1661-1670
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• 1996

Incompressible flow over a backward-facing step is computed by low Reynolds number turbulence models in order to compare with direct simulation results. In this study, selected low Reynolds number 1st and 2nd (Algebraic Stress Model : ASM) moment closure turbulence models are adopted and compared with each other. Each turbulence model predicts different flow characteristics, different re-attachment point, velocity profiles and Reynolds stress distribution etc. Results by .kappa.-.epsilon. turbulence models indicate that predicted re-attachment lengths are shorter than those by standard model. Turbulent intensity and eddy viscosity by low Reynolds number .kappa.-.epsilon. models are still greater than DNS results. The results by algebraic stress model (ASM) are more reasonable than those by .kappa.-.epsilon. models. The convective scheme is QUICK (Quadratic Upstream Interpolation for Convective Kinematics) and SIMPLE algorithm is adopted. Reynolds number based on step height and inlet free stream velocity is 5100.