• Title/Summary/Keyword: neural network.

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Control of the Multi-Mode Muffler for Low Noise and Low Backpressure (저소음 저배압을 위한 다중모드 배기계의 소음제어)

  • Son, Dong-Gu;Kim, Heung-Seop;O, Jae-Eung
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.24 no.6 s.177
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    • pp.1371-1378
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    • 2000
  • To reduce backpressure for improving engine power in exhaust system, a large difference of pressure is required, but this is in conflict with the requirement for reducing exhaust noise that needs a small pressure difference. In this paper, the controllable muffler designed by simplifying the structure of the exhaust system has a low backpressure and a proper sound specification to the rotation of engine. The exhaust system in conventional studies has been designed to have maximum noise reduction over the whole driving domain, but due to its complex structure this led to increased backpressure. If the muffler is designed according to the driving frequency, which is a dominant noise component in stationary driving speed, the backpressure is reduced due to the simplified structure of the muffler. Furthermore, a multi-mode muffler able to change structure with varied driving speed was designed.

Developing Artificial Neurons Using Carbon Nanotubes Smart Composites (탄소나노튜브 스마트 복합소재를 이용한 인공뉴런 개발 연구)

  • Kang, In-Pil;Baek, Woon-Kyung;Choi, Gyeong-Rak;Jung, Joo-Young
    • Proceedings of the KSME Conference
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    • 2007.05a
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    • pp.136-141
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    • 2007
  • This paper introduces an artificial neuron which is a nano composite continuous sensor. The continuous nano sensor is fabricated as a thin and narrow polymer film sensor that is made of carbon nanotubes composites with a PMMA or a silicone matrix. The sensor can be embedded onto a structure like a neuron in a human body and it can detect deteriorations of the structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensor can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods. The artificial neuron is expected to effectively detect damage in large complex structures including composite helicopter blades and composite aircraft and vehicles.

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Adjustment of Roll Gap for the Dimension Accuracy of Bar in Hot Bar Rolling Process

  • Kim, Dong-Hwan;Kim, Byung-Min;Lee, Youngseog
    • International Journal of Precision Engineering and Manufacturing
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    • v.4 no.1
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    • pp.56-62
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    • 2003
  • The objective of this study is to adjust the roll gap for the dimension accuracy of bar in hot bar rolling process considering roll wear. In this study hot bar rolling processes for round and oval passes have been investigated. In order to predict the roll wear, the wear model is reformulated as an incremental form and then wear depth of roll is calculated at each deformation step on contact area using the results of finite element analysis, such as relative sliding velocity and normal pressure at contact area. Archard's wear model was applied to predict the roll wear. To know the effects of thermal softening of DCI (Ductile Cast Iron) roll material according to operating conditions, high temperature micro hardness test is executed and a new wear model has been proposed by considering the thermal softening of DCI roll expressed in terms of the main tempering curve. The new technique developed in this study for adjusting roll gap can give more systematically and economically feasible means to improve the dimension accuracy of bar with full usefulness and generality.

A Study on the Digital Signal Processing for the Pattern fiecognition of Weld Flaws (용접결함의 패턴인식을 위한 디지털 신호처리에 관한 연구)

  • 김재열;송찬일;김병현
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.10a
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    • pp.393-396
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    • 1995
  • In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

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Parameter Estimation of 2-DOF System Based on Unscented Kalman Filter (UKF 기반 2-자유도 진자 시스템의 파라미터 추정)

  • Seung, Ji-Hoon;Kim, Tae-Yeong;Atiya, Amir;Parlos, Alexander;Chong, Kil-To
    • Journal of the Korean Society for Precision Engineering
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    • v.29 no.10
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    • pp.1128-1136
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    • 2012
  • In this paper, the states and parameters in a dynamic system are estimated by applying an Unscented Kalman Filter (UKF). The UKF is widely used in various fields such as sensor fusion, trajectory estimation, and learning of Neural Network weights. These estimations are necessary and important in determining the stability of a mobile system, monitoring, and predictions. However, conventional approaches are difficult to estimate based on the experimental data, due to properties of non-linearity and measurement noises. Therefore, in this paper, UKF is applied in estimating the states and parameters needed. An experimental dynamic system has been set up for obtaining data and the experimental data is collected for parameter estimation. The measurement noises are primarily reduced by applying the Low Pass Filter (LPF). Given the simulation results, the estimated error rate is 39 percent more efficient than the results obtained using the Least Square Method (LSM). Secondly, the estimated parameters have an average convergence period of four seconds.

Tonality Design for Sound Quality Evaluation for Gear Whine Sound (승합차량의 액슬기어 음질의 평가를 위한 새로운 순음도 모델 개발과 응용)

  • Kim, Eui-Youl;Jang, Ji-Uk;Lee, Sang-Kwon
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.22 no.12
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    • pp.1172-1183
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    • 2012
  • Aure's tonality was considered as the sound metrics for the expression of the tonality of gear whine sound in a previous research. It was failed to use the Aure's tonality as a sound metric for the tonal impression. Thus Aures's tonality, was developed for tonal impression in previous research. However, this metric did not express well the tonality of gear whine sound since the whine sound is a non-stationary signal with frequency modulation and amplitude modulation. In this study, the new method for the tonality evaluation for a non-stationary signal is presented. It is developed based on the prominence ratio, tonality impression function, and lower threshold level. It improves the accuracy and reliability of the sound quality index being used for the sound quality evaluation of the axle-gear whine sound.

Sound Quality Evaluation and Grade Construction of the Level D Noise for the Vehicle Using MTS (MTS기법을 이용한 차량 D단 소음의 음질 평가 및 음질 등급화 구축)

  • Park, Sang-Gil;Park, Won-Sik;Sim, Hyoun-Jin;Lee, Jung-Youn;Oh, Jae-Eung
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.18 no.4
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    • pp.393-399
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    • 2008
  • The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

Performance Evaluation of Car Model Recognition System Using HOG and Artificial Neural Network (HOG와 인공신경망을 이용한 자동차 모델 인식 시스템 성능 분석)

  • Park, Ki-Wan;Bang, Ji-Sung;Kim, Byeong-Man
    • Journal of Korea Society of Industrial Information Systems
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    • v.21 no.5
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    • pp.1-10
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    • 2016
  • In this paper, a car model recognition system using image processing and machine learning is proposed and it's performance is also evaluated. The system recognizes the front of car because the front of car is different for every car model and manufacturer, and difficult to remodel. The proposed method extracts HOG features from training data set, then builds classification model by the HOG features. If user takes photo of the front of car, then HOG features are extracted from the photo image and are used to determine the model of car based on the trained classification model. Experimental results show a high average recognition rate of 98%.

Optimal R Wave Detection and Advanced PVC Classification Method through Extracting Minimal Feature in IoT Environments (IoT 환경에서 최적 R파 검출 및 최소 특징점 추출을 통한 향상된 PVC 분류방법)

  • Cho, Iksung;Woo, Dongsik
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.13 no.4
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    • pp.91-98
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    • 2017
  • Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

Development of an On-line Intelligent Embedded System for Detection the Leakage of Pipeline (실시간 누수 감지 가능한 매립형 지능형 배관 진단 시스템)

  • Lee, Changgil;Kim, Tae-Heon;Chang, Hajoo;Park, Seunghee
    • 한국방재학회:학술대회논문집
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    • 2011.02a
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    • pp.94-94
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    • 2011
  • 배관 구조물에서는 내부 미세 균열에서부터 국부 좌굴, 볼트 풀림, 피로 균열 등과 같이 다양한 형태의 손상이 복합적으로 발생 가능하다. 이러한 복합 손상은 배관 구조물의 누수, 누유 등의 사고를 야기할 수 있다. 하지만 기존의 단일 스케일 계측 시스템으로부터 복합 손상에 의한 실시간 누수를 진단하기는 매우 어렵다. 본 연구 단계에서는 누수를 야기하는 복합 손상을 효율적으로 진단하기 위하여 선행 연구에서 제안된 압전센서를 이용한 자가 계측 회로 기반의 다중 스케일 계측 시스템을 구조물의 복합 손상 진단에 적용하였다. 자가 계측 회로 기반 다중 스케일 계측 시스템은 크게 두 가지 형태의 신호를 계측한다. 첫 번째 스케일은 임피던스 계측으로부터 특정 주파수 대역폭에 대한 구조 응답을 계측하며, 두 번째 스케일은 유도 초음파 계측으로부터 단일 중심 주파수에 해당하는 구조물의 응답을 계측한다. 복합 손상을 손상 유형별로 분류하기 위하여 E/M 임피던스(Electro-mechanical impedance)및 유도 초음파(Guided wave) 계측으로부터 추출한 특성을 이용하여 2차원 손상지수를 계산하고 이를 지도학습 기반 패턴인식 기법(Supervised learning based pattern recognition) 중 확률론적 신경망 기법(Probabilistic Neural Network, PNN)에 적용한다. 제안된 기법의 적용성 검토를 위하여 파이프 구조물에 인위적으로 다중 손상을 생성시켜 시험을 수행하였다. 본 연구에서 제안된 기법이 실제 배관 구조물에 성공적으로 적용된다면 손상 부재의 거동 및 구조물 성능의 손상에 대한 영향을 효율적으로 진단하고 평가함으로써 배관 구조물의 효과적인 유지관리가 가능할 것으로 예상된다.

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