• Title/Summary/Keyword: Output Estimation

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Real Time ECG Derived Respiratory Extraction from Heart Rate for Single Lead ECG Measurement using Conductive Textile Electrode (전도성 직물을 이용한 단일 리드 심전도 측정 및 실시간 심전도 유도 호흡 추출 방법에 관한 연구)

  • Yi, Kye-Hyoung;Park, Sung-Bin;Yoon, Hyoung-Ro
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.55 no.7
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    • pp.335-343
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    • 2006
  • We have designed the system that measure one channel ECG by two electrode and extract real-time EDR with more related resipiration and comportable to subject by using conductive textile. On the assumption that relation between RL electrode and potential measurement electrode is coupled with RC connected model, we designed RL drive output to feedback two electrode for reduction of common mode signal. The conductive textile which was used for two ECG electrode was offered more comfort during night sleep in bed than any other method using attachments. In the method of single-lead EDR, R wave point or QRS interval area could be used for EDR estimation in traditional method, it is, so to speak, the amplitude modulation(AM) method for EDR. Alternatively, R-R interval could be used for frequency modulation(FM) method based on Respiratory Sinus Arrhythmia(RSA). For evaluation of performance on AM EDR and FM EDR from 14 subject, ECG lead III was measured. Each EDR was compared with both temperature around nose(direct measurement of respiration) and respiration signal from thoracic belt(indirect measurement of respiration) on mean squared error(MSE), cross correlation(Xcorr), and Coherence. The upsampling interpolation technique of multirate signal processing is applied to interpolating data instead of cubic spline interpolation. As a result, we showed the real-time EDR extraction processing to be implemented at micro-controller.

Lane Detection for Adaptive Control of Autonomous Vehicle (지능형 자동차의 적응형 제어를 위한 차선인식)

  • Kim, Hyeon-Koo;Ju, Yeonghwan;Lee, Jonghun;Park, Yongwan;Jeong, Ho-Yeol
    • IEMEK Journal of Embedded Systems and Applications
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    • v.4 no.4
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    • pp.180-189
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    • 2009
  • Currently, most automobile companies are interested in research on intelligent autonomous vehicle. They are mainly focused on driver's intelligent assistant and driver replacement. In order to develop an autonomous vehicle, lateral and longitudinal control is necessary. This paper presents a lateral and longitudinal control system for autonomous vehicle that has only mono-vision camera. For lane detection, we present a new lane detection algorithm using clothoid parabolic road model. The proposed algorithm in compared with three other methods such as virtual line method, gradient method and hough transform method, in terms of lane detection ratio. For adaptive control, we apply a vanishing point estimation to fuzzy control. In order to improve handling and stability of the vehicle, the modeling errors between steering angle and predicted vanishing point are controlled to be minimized. So, we established a fuzzy rule of membership functions of inputs (vanishing point and differential vanishing point) and output (steering angle). For simulation, we developed 1/8 size robot (equipped with mono-vision system) of the actual vehicle and tested it in the athletics track of 400 meter. Through the test, we prove that our proposed method outperforms 98 % in terms of detection rate in normal condition. Compared with virtual line method, gradient method and hough transform method, our method also has good performance in the case of clear, fog and rain weather.

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Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches

  • Yavuz, Gunnur
    • Structural Engineering and Mechanics
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    • v.57 no.4
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    • pp.657-680
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    • 2016
  • Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models

  • Ozcan, Giyasettin;Kocak, Yilmaz;Gulbandilar, Eyyup
    • Computers and Concrete
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    • v.19 no.3
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    • pp.275-282
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    • 2017
  • The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

Mathematical Modeling & Empirical Analysis for Estimation of Fuel Consumption using OBD-II Data in Vehicle (차량 OBD-II 데이터를 이용한 연료 소모량 추정의 수식적 모델링 및 실증 분석)

  • Lee, Min-Goo;Park, Yong-Guk;Jung, Kyung-Kwon;Yoo, Jun-Jae
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.48 no.2
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    • pp.9-14
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    • 2011
  • This Paper proposed the prediction method of fuel consumption from vehicle informations through OBD-II Interface. We assumed RPM, TPS had a relationship with fuel consumption. We got the output as fuel-consumption from a vehicle RPM, TPS as input by using polynomial equation. We had modelling as quadric function with OBD-II data and fuel consumption data supported by automotive company in real. In order to verify the effectiveness of proposed method, 5 km real road-test was performed. The results showed that the proposed method can estimate precisely the fuel consumption from vehicle multi-data.

Estimation of fishing power and fishing capacity on coastal stow net fishery in the Korean waters (연안개량안강망 어업의 어획성능 및 어획능력 추정)

  • KIM, Pyungkwan;LEE, Kyounghoon;KIM, Dohoon;LEE, Geonho;AN, Heui-Chun;KIM, Seonghun;YANG, Yongsu
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.51 no.4
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    • pp.583-591
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    • 2015
  • The coastal stow net (stow net hereafter) in Korea is one of the major fishing methods for yellow croaker (Larimichthys polyactis), ribbon fish (Trichiurus lepturus), and anchovy (Engraulis japonicus). In terms of energy efficiency, the stow net fishery is more competitive than towing fishing gears such as trawl gears. The fishing vessels in stow net fishery have consumed less fossil fuel and also have had less carbon dioxide emission into the atmosphere. however, the stow net fishery is necessary to be regulated due to its increased output of the fleet. Therefore, it is required for fisheries authorities to manage the fishing capacity or fishing power for the assurance of fishery's sustainability. For fisheries management authorities, it is necessary to quantify data related to fishing capacity and fishing power to deploy fishery policy in a sustainable way. In terms of data for decision-making, Data envelopment analysis (DEA) method was conducted to estimate fishing capacity. Fishing power index (FPI) was also applied to calculate relative fishing power to approach the problem in a quantitative way.

Evaluation of the Closed-type Sprinkler Head Activation Time (밀폐형 스프링클러 헤드의 동작시간 평가)

  • Moon-Hak, Jee;Sung-Yull, Hong
    • Fire Science and Engineering
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    • v.18 no.3
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    • pp.1-8
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    • 2004
  • As a predominant active fire suppression method, closed-type sprinkler systems are used for the purpose of fire control and suppression at the nuclear power plants as well as the industrial facilities. It goes without saying that the proper selection of the system guarantees the adequate actuation of the thermal device. Consequently, the appropriate evaluation should be executed for the thermal behavior with the theoretical and empirical approach. For this purpose, the comparison of activation time for the fusible-link type sprinkler head with the simplified fire case and t-square fire growth case was evaluated. At this paper, the comparison output was presented with the tendency of thermal behavior. In addition, we issued some technical comments for the most appropriate equation in case of the estimation of the sprinkler head activation time. We also raised some idea that should be incorporated for the usage of the t-square equation for the realistic application in the field of the performance-base fire protection approach.

Estimation of Sweet Pepper Crop Fresh Weight with Convolutional Neural Network (합성곱 신경망을 이용한 온실 파프리카의 작물 생체중 추정)

  • Moon, Taewon;Park, Junyoung;Son, Jung Eek
    • Journal of Bio-Environment Control
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    • v.29 no.4
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    • pp.381-387
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    • 2020
  • Various studies have been attempted to estimate and measure the fresh weight of crops. However, no studies have used raw images of sweet peppers to estimate fresh weight. Recently, image processing research using convolution neural network (CNN) that can use raw data is increasing. In this study, the crop fresh weight was estimated by using the images of sweet peppers as inputs of CNN. The experiment was performed in a greenhouse growing sweet pepper (Capsicum annuum L.). The fresh weight, the output of the CNN, was regressed based on the data collected through destructive investigation. The highest coefficient of determination (R2) of the trained CNN was 0.95. The estimated fresh weight showed a very similar trend to the actual measured value.

Decision of Neural Network Architecture for Software Development Effort Estimation using Prior Information (사전 정보를 이용한 소프트웨어 개발노력 추정 신경망 구조 결정)

  • 박석규;유창열;박영목
    • Journal of the Korea Computer Industry Society
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    • v.2 no.9
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    • pp.1191-1198
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    • 2001
  • An increasingly important facet of software development is the ability to estimate the associate cost and effort of development early in the development life cycle. Most of the proposed models are based upon a combination of intuition, expert judgement, and regression analysis of empirical data. Overall, the work has failed to produce any single model that can be applied with a reasonable degree of success to a variety of environments. This paper presents a neural network (NN) model that related software development effort to software size measured in function element types. The heuristic approach is applied to decide the number of hidden neurons in NN from the relationship between input-output pairs. The research describes appropriate NN modeling in the context of a case study for 24 software development projects. Also, this paper compared the NN model with a regression analysis model and found the NN model has better accuracy.

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Low Cost Speed Control System of PM Brushless DC Motor Using 2 Hall-ICs (2Hall-ICs를 이용한 저가형 PM Brushless DC Motor 속도 제어)

  • 윤용호;우무선;김덕규;원충연;최유영
    • The Transactions of the Korean Institute of Power Electronics
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    • v.9 no.4
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    • pp.311-318
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    • 2004
  • Generally, PM BLDC drive system is necessary that the three Hall-ICs evenly be distributed around the stator circumference and encoder installed in case of the 3 phase motor. The Hall-ICs are set up in this motor to detect the main flux from the rotor. So the output signal from Hall-ICs is used to drive a power transistor to control the stator winding current. Instead of using three Hall-ICs and encoder, this paper uses only two Hall-ICs for the permanent magnet rotor position and for the speed feedback signals, and uses a micro controller of 16-bit type(80C196KC) with the 3 phase PM BLDC whose six stator and two rotor designed. Two Hall-IC Hc and $H_B$ are placed on the endplate at 120 degree phase difference. With these elements, we estimate information of the other phase in sequence through a rotating rotor.