• Title/Summary/Keyword: Error Pattern Modeling

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Implementation of Path Finding Method using 3D Mapping for Autonomous Robotic (3차원 공간 맵핑을 통한 로봇의 경로 구현)

  • Son, Eun-Ho;Kim, Young-Chul;Chong, Kil-To
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.2
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    • pp.168-177
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    • 2008
  • Path finding is a key element in the navigation of a mobile robot. To find a path, robot should know their position exactly, since the position error exposes a robot to many dangerous conditions. It could make a robot move to a wrong direction so that it may have damage by collision by the surrounding obstacles. We propose a method obtaining an accurate robot position. The localization of a mobile robot in its working environment performs by using a vision system and Virtual Reality Modeling Language(VRML). The robot identifies landmarks located in the environment. An image processing and neural network pattern matching techniques have been applied to find location of the robot. After the self-positioning procedure, the 2-D scene of the vision is overlaid onto a VRML scene. This paper describes how to realize the self-positioning, and shows the overlay between the 2-D and VRML scenes. The suggested method defines a robot's path successfully. An experiment using the suggested algorithm apply to a mobile robot has been performed and the result shows a good path tracking.

Unsteady Internal Flow Analysis of a Cathode Air Blower Used for Fuel Cell System (연료전지용 캐소드 공기블로어의 비정상 내부유동장 연구)

  • Jang, Choon-Man;Lee, Jong-Sung
    • New & Renewable Energy
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    • v.8 no.3
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    • pp.6-13
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    • 2012
  • This paper describes unsteady internal flow characteristics of a cathode air blower, used for the 1 kW fuel cell system. The cathode air blower considered in the present study is a diaphragm type blower. To analyze the flow field inside the diaphragm cavity, compressible unsteady numerical simulation is performed. Moving mesh system is applied to the numerical analysis for describing the volume change of the diaphragm cavity in time. Throughout a numerical simulation by modeling the inlet and outlet valves in a diaphragm cavity, unsteady nature of an internal flow is successfully analyzed. Variations of mass flow rate, force and pressure on the lower moving plate of a diaphragm cavity are evaluated in time. The computed mass flow rate at the same pressure and rotating frequency of a motor has a maximum of 5 percent error with the experimental data. It is found that flow pattern at the suction process is more complex compared to that at the discharge process. Unsteady nature of internal flow in the cathode air blower is analyzed in detail.

Modeling of Cylinder Expansion Test Using JWL Equation of State (JWL 상태방정식을 활용한 실린더 팽창 실험 모델링)

  • Minju, Kim;Sangki, Kwon
    • Explosives and Blasting
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    • v.41 no.1
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    • pp.19-31
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    • 2023
  • There are various types of explosives, and each explosive has different characteristics such as water resistance, energy required for detonation, and crushing power, so understanding the characteristics of explosives is important for safe use and performance improvement. Computer simulation is used indirectly along with various experiments to understand the characteristics of explosives, and a state equation is used to express the explosive detonation process through computer simulation. In this study, the explanation of JWL EOS, which is mainly used among the state equations of explosives, and the cylinder expansion experiment to calculate the coefficient of JWL EOS were implemented as ANSYS AUTODYN and compared and analyzed with the actual experimental results. As a result, an error rate of around 20% occurred, and it was found that the overall change pattern of pressure and energy was consistent with the previously published experimental results.

Development of Removal Techniques for PRC Outlier & Noise to Improve NDGPS Accuracy (국토해양부 NDGPS 정확도 향상을 위한 의사거리 보정치의 이상점 및 노이즈 제거기법 개발)

  • Kim, Koon-Tack;Kim, Hye-In;Park, Kwan-Dong
    • Journal of Korean Society for Geospatial Information Science
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    • v.19 no.2
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    • pp.63-73
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    • 2011
  • The Pseudorange Corrections (PRC), which are used in DGPS as calibration messages, can contain outliers, noise, and anomalies, and these abnormal events are unpredictable. When those irregular PRC are used, the positioning error gets higher. In this paper, we propose a strategy of detecting and correcting outliers, noise, and anomalies by modeling the changing pattern of PRC through polynomial curve fitting techniques. To validate our strategy, we compared positioning errors obtained without PRC calibation with those with PRC calibration. As a result, we found that our algorithm performs very well; the horizontal RMS error was 3.84 m before the correction and 1.49 m after the correction.

Modeling of the driving pattern for energy saving of the railway vehicles (철도차량의 주행에너지 절약을 위한 열차 주행 패턴 모델링)

  • Kim, Jung-Hyun;Kim, Sang-Hoon;Shin, Han-Chul;Lee, Se-Hoon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2011.01a
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    • pp.107-108
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    • 2011
  • Since the development of railway technology, the current urban Railway the first train line in the country for safe operation control automatic/unattended operation, automatic train operation equipment available (ATO) on time and reliable operation has introduced. ATO Automatic operation controlled by the value (Target velocity) and the feedback value (Actual velocity) by the error between the backing and braking of the train by repeated low energy efficiency. In this paper, given a fixed distance stations between time operation with minimal energy in the driving characteristics and driving trains are modeled. Therefore, in line 5 real route time sectional drive straight sections for experimental data analysis / draft Section / curved and section of the train on that line is selected according to the changing driving patterns to minimize the energy optimal driving patterns were presented.

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Classification and Performance Evaluation Methods of an Algal Bloom Model (적조모형의 분류 및 성능평가 기법)

  • Cho, Hong-Yeon;Cho, Beom Jun
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.26 no.6
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    • pp.405-412
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    • 2014
  • A number of algal bloom models (red-tide models) have been developed and applied to simulate the redtide growth and decline patterns as the interest on the phytoplankton blooms has been continuously increased. The quantitative error analysis of the model is of great importance because the accurate prediction of the red-tide occurrence and transport pattern can be used to setup the effective mitigations and counter-measures on the coastal ecosystem, aquaculture and fisheries damages. The word "red-tide model" is widely used without any clear definitions and references. It makes the comparative evaluation of the ecological models difficult and confusable. It is highly required to do the performance test of the red-tide models based on the suitable classification and appropriate error analysis because model structures are different even though the same/similar words (e.g., red-tide, algal bloom, phytoplankton growth, ecological or ecosystem models) are used. Thus, the references on the model classification are suggested and the advantage and disadvantage of the models are also suggested. The processes and methods on the performance test (quantitative error analysis) are recommend to the practical use of the red-tide model in the coastal seas. It is suggested in each stage of the modeling procedures, such as verification, calibration, validation, and application steps. These suggested references and methods can be attributed to the effective/efficient marine policy decision and the coastal ecosystem management plan setup considering the red-tide and/or ecological models uncertainty.

Soil moisture estimation using the water cloud model and Sentinel-1 & -2 satellite image-based vegetation indices (Sentinel-1 & -2 위성영상 기반 식생지수와 Water Cloud Model을 활용한 토양수분 산정)

  • Chung, Jeehun;Lee, Yonggwan;Kim, Jinuk;Jang, Wonjin;Kim, Seongjoon
    • Journal of Korea Water Resources Association
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    • v.56 no.3
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    • pp.211-224
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    • 2023
  • In this study, a soil moisture estimation was performed using the Water Cloud Model (WCM), a backscatter model that considers vegetation based on SAR (Synthetic Aperture Radar). Sentinel-1 SAR and Sentinel-2 MSI (Multi-Spectral Instrument) images of a 40 × 50 km2 area including the Yongdam Dam watershed of the Geum River were collected for this study. As vegetation descriptor of WCM, Sentinel-1 based vegetation index RVI (Radar Vegetation Index), depolarization ratio (DR), and Sentinel-2 based NDVI (Normalized Difference Vegetation Index) were used, respectively. Forward modeling of WCM was performed by 3 groups, which were divided by the characteristics between backscattering coefficient and soil moisture. The clearer the linear relationship between soil moisture and the backscattering coefficient, the higher the simulation performance. To estimate the soil moisture, the simulated backscattering coefficient was inverted. The simulation performance was proportional to the forward modeling result. The WCM simulation error showed an increasing pattern from about -12dB based on the observed backscattering coefficient.

An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost (비대칭 오류비용을 고려한 분류기준값 최적화와 SVM에 기반한 지능형 침입탐지모형)

  • Lee, Hyeon-Uk;Ahn, Hyun-Chul
    • Journal of Intelligence and Information Systems
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    • v.17 no.4
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    • pp.157-173
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    • 2011
  • As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

Calibrating Stereoscopic 3D Position Measurement Systems Using Artificial Neural Nets (3차원 위치측정을 위한 스테레오 카메라 시스템의 인공 신경망을 이용한 보정)

  • Do, Yong-Tae;Lee, Dae-Sik;Yoo, Seog-Hwan
    • Journal of Sensor Science and Technology
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    • v.7 no.6
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    • pp.418-425
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    • 1998
  • Stereo cameras are the most widely used sensing systems for automated machines including robots to interact with their three-dimensional(3D) working environments. The position of a target point in the 3D world coordinates can be measured by the use of stereo cameras and the camera calibration is an important preliminary step for the task. Existing camera calibration techniques can be classified into two large categories - linear and nonlinear techniques. While linear techniques are simple but somewhat inaccurate, the nonlinear ones require a modeling process to compensate for the lens distortion and a rather complicated procedure to solve the nonlinear equations. In this paper, a method employing a neural network for the calibration problem is described for tackling the problems arisen when existing techniques are applied and the results are reported. Particularly, it is shown experimentally that by utilizing the function approximation capability of multi-layer neural networks trained by the back-propagation(BP) algorithm to learn the error pattern of a linear technique, the measurement accuracy can be simply and efficiently increased.

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An Empiricl Study on the Learnign of HMM-Net Classifiers Using ML/MMSE Method (ML/MMSE를 이용한 HMM-Net 분류기의 학습에 대한 실험적 고찰)

  • Kim, Sang-Woon;Shin, Seong-Hyo
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.6
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    • pp.44-51
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    • 1999
  • The HMM-Net is a neural network architecture that implements the computation of output probabilities of a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria of maximum likehood(ML) and minimization of mean squared error(MMSE) are used for learning HMM-Net classifiers. The criterion MMSE is better than ML when initial learning condition is well established. However Ml is more useful one when the condition is incomplete[3]. Therefore we propose an efficient learning method of HMM-Net classifiers using a hybrid criterion(ML/MMSE). In the method, we begin a learning with ML in order to get a stable start-point. After then, we continue the learning with MMSE to search an optimal or near-optimal solution. Experimental results for the isolated numeric digits from /0/ to /9/, a training and testing time-series pattern set, show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.

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