• Journal of the Korean Society of Combustion
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• v.16 no.2
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• pp.40-46
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• 2011

The diesel pre-heater has being used in cabin heating and coolant heating of engine to reduce the engine warm up time for commercial vehicle. The pre-heaters are classified as diesel spray combustor and it forms diffusion flame. By using swirler, a recirculation flow of hot product gases is established near the fuel nozzle and it helps the maintaining of diffusion flame. The design difference of swirler can affect on reaction characteristics and temperature distribution inside pre-heater. The purpose of this study is the investigation of the effect of swirler configuration on combustion characteristics. To solve spray combustion problem, the Euler-Lagrange approach discrete model is used to track droplet trajectory and evaporation history. The PDF equilibrium model is used for chemical reaction model. These models are implemented into the FLUENT code.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.384-391
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• 2000

Most of the previous works on classical location models are based on the assumption that the value(or utilities) of inventory remains constants over time. In this study a special case of location problem is studied for both ameliorating and deteriorating items in two-echelon supply-chain management such as agricultural and fishery products. The objective of this study is to determine the minimum number of storage facilities among a discrete set of location sites so that the probability for each customer to be covered is not less than a critical value. We have formulated this problem using stochastic set-covering problem which can be solved by 0-1 programming method. Also we developed a computer program and applied to a set of problems for fish culture storage and distribution centers and the sample results well show the impact of ameliorating and deteriorating rate on the location problem. For the further study, a graphical user-interface with visualization for input and output is needed to be developed.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.180-188
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• 1998

Z-map is a special form of discrete nonparametric representation in which the height values at grid points on the xy-plane are stored as a 2D array z[i.j]. While z-map is the simplest form of representing sculptured surfaces and it is the most versatile scheme for modeling nonparametric objects, its practical application in industry (eg, tool-path generation) aroused much controversy over its weaknesses ; accuracy, singularity (eg, vertical wall), and some excessive storage needs. Although z-map has such limitations, much research on the application of z-map can be found in various articles. However, research on the systematic analysis of sculptured surface shape representation via z-map model is rather rare. Presented in this paper are the following: shape modeling power of the simple z-map model, exact (within tolerance) B-map representation of sculptured surfaces which have some feature-shapes such as vertical-walls and real sharp-edges by adopting some complementary B-map models, and some application examples.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.335-344
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• 2001

Texture is extensively studied in a variety of image processing applications such as image segmentation and classification because it is an important property to perceive regions and surfaces. This paper focused on the analysis and synthesis of textured single and multiband images using Markov Random Field model considering the existent spatial correlation. Especially, for multiband images, the cross-channel correlation existing between bands as well as the spatial correlation within band should be considered in the model. Although a local interaction is assumed between the specified neighboring pixels in MRF models, during the maximization process, short-term correlations among neighboring pixels develop into long-term correlations. This result in exhibiting phase transition. In this research, the role of temperature to obtain the most probable state during the sampling procedure in discrete Markov Random Fields and the stopping rule were also studied.

• Journal of the Korea Society for Simulation
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• v.11 no.4
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• pp.91-105
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• 2002

The attackers on Internet-connected systems we are seeing today are more serious and technically complex than those in the past. So it is beyond the scope of amy one system to deal with the intrusions. That the multiple IDSes (Intrusion Detection System) coordinate by sharing attacker's information for the effective detection of the intrusion is the effective method for improving the intrusion detection performance. The system which uses BBA (BlackBoard Architecture) for the information sharing can be easily expanded by adding new agents and increasing the number of BB (BlackBoard) levels. Moreover the subdivided levels of blackboard enhance the sensitivity of the intrusion detection. For the simulation, security models are constructed based on the DEVS (Discrete EVent system Specification) formalism. The intrusion detection agent uses the ES (Expert System). The intrusion detection system detects the intrusions using the blackboard and the firewall responses these detection information.

• Elastomers and Composites
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• v.53 no.4
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• pp.207-212
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• 2018

Taylor rod impact tests have been the subject of many theoretical and experimental investigations. This paper discusses the numerical methods for simulating the Taylor impact test, which is widely used to obtain constitutive equations and failure conditions under high-velocity collisions of materials. With this in mind, a particle-based MPM (material point method) for linear viscoelastic solid materials was implemented, and MPM simulations for viscoelastic deformation behavior were numerically verified and confirmed by comparing the MPM and FEM results. In addition, this modeling and numerical approach could be extended to more complex viscoelastic models for basic understanding and to analyze the deformation and fracture behavior of more complicated viscoelastic material systems.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• Annual Conference of KIPS
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• 2009.04a
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• pp.148-151
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• 2009

u-GIS 공간정보를 제공하기 위한 3D 모델의 렌더링 성능을 향상시키기 위해 일반적으로 Discrete LOD 기법이 적용된다. 하지만 u-GIS의 3D 건물 모델은 그 복잡성이 매우 높아 LOD 단계별로 모델을 직접 생성하려면 많은 비용과 시간이 소요되어 효율적이지 못하다. 본 연구에서는 원본 3D 모델을 활용하여 하위 LOD 단계의 모델을 메쉬 간략화 알고리즘인 QEM 기법을 통해 생성하는 프로그램을 구현하였다. 프로그램은 다양한 3D 모델의 데이터 포맷을 입력받고 출력할 수 있도록 하여 범용성을 높이고, 생성되는 모델의 결과를 바로 화면으로 확인할 수 있도록 하여 사용자 편의성을 확보하였다. 몇 개의 실제 3D 건물 모델로 실험을 수행하여 프로그램의 성능을 검증하고 그 결과를 도출하였다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.10-11
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• 2021

Demands for digital transformation of the construction industry are increasing to improve the accuracy of the construction operation planning and the performance of the construction operation. Even though large number of studies are being conducted to this date, most of the studies are not likely to be available on the real sites. Therefore, this study provides construction managers with a methodology of drawing construction performance indicators based on productivity analysis using Artificial Neural Network (ANN) models and Web-CYCLONE. This methodology is expected to have high utilization and precision of construction operation planning and management.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.553-556
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• 2020

최근 이미지 인식을 위한 Convolutional Neural Network(CNN) 모델의 경량화에 관한 연구가 활발하게 이루어지고 있다. 그중 양자화는 모델을 구성하는 가중치의 크기를 낮추는 방법이다. 기존의 CNN 모델에서 가장 큰 비중을 하는 Fully Connected Layer(FCL)는 내부적으로 32 Bit의 실수 행렬로 표현된다. 본 논문에서는 미리 학습된 실수 가중치를 더 작은 비트의 정수 행렬로 양자화한다. 양자화된 행렬에 대해서 영상 압축 등에서 사용하는 Discrete Cosine Transform(DCT)을 통해 주파수 영역으로 변환한 후 고주파 영역을 생략하는 손실압축 방법을 제안한다. 실험을 통해 그 과정에서 손실에 따른 정확도의 변화를 나타낸다.