• Title/Summary/Keyword: online estimation

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A miniaturized attitude estimation system for a gesture-based input device with fuzzy logic approach

  • Wook Chang;Jing Yang;Park, Eun-Seok;Bang, Won-Chul;Kang, Kyoung-Ho;Cho, Sung-Jung;Kim, Dong-Yoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09a
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    • pp.616-619
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    • 2003
  • In this paper, we develop an input device equipped with accelerometers and gyroscopes. The installed sensors measure the inertial measurements i.e., accelerations and angular rates produced by the movement of the system when a user is writing on the plane surface or in the three dimensional space. The gyroscope measurement are integrated once to give the attitude of the system and consequently used to remove the gravity included in the acceleration measurements. The compensated accelerations bin doubly integrated to yield the position of the system. Due to the integration processes involved in recovering the users'motions, the accuracy of the position estimation significantly deteriorates with time. Among various error sources of the system incorrect estimation of attitude causes the largest portion of the positioning error since the gravity is not fully cancelled. In order to solve this problem, we propose a Kalman filler-based attitude estimation algorithm which fuses measurement data from accelerometers and gyroscopes by fuzzy logic approach. In addition, the online calibration of the gyroscope biases are performed in parallel with the attitude estimation to give more accurate attitude estimation. The effectiveness and the feasibility of the presented system is demonstrated through computer simulations and actual experiments.

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A Study on Political Attitude Estimation of Korean OSN Users (온라인 소셜네트워크를 통한 한국인의 정치성향 예측 기법의 연구)

  • Wijaya, Muhammad Eka;Ahn, Heejune
    • Journal of Korea Society of Industrial Information Systems
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    • v.21 no.4
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    • pp.1-11
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    • 2016
  • Recently numerous studies are conducted to estimate the human personality from the online social activities. This paper develops a comprehensive model for political attitude estimation leveraging the Facebook Like information of the users. We designed a Facebook Crawler that efficiently collects data overcoming the difficulties in crawling Ajax enabled Facebook pages. We show that the category level selection can reduce the data analysis complexity utilizing the sparsity of the huge like-attitude matrix. In the Korean Facebook users' context, only 28 criteria (3% of the total) can estimate the political polarity of the user with high accuracy (AUC of 0.82).

RCGA-Based Parameter Estimation of Solar Cell Models (RCGA에 기초한 태양전지 모델의 파라미터 추정)

  • 권봉재;신명호;손영득;진강규
    • Journal of Advanced Marine Engineering and Technology
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    • v.27 no.6
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    • pp.696-703
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    • 2003
  • A photovoltaic power generation system is an infinite and clean energy system. Recently. because of the realization of high efficiency and low cost PV modules, the studies on the PV system have extensively increased. In this paper. we present an online scheme for parameter estimation of solar cell, based on the model adjustment technique and a real-coded genetic algorithm(RCGA). The ideal diode model and the diode model with series and shunt resistors are used to estimate their parameters, Simulation works using field data in the form of a V-I characteristic curve are carried out to demonstrate the effectiveness of the proposed method.

State-of-charge Estimation for Lithium-ion Battery using a Combined Method

  • Li, Guidan;Peng, Kai;Li, Bin
    • Journal of Power Electronics
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    • v.18 no.1
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    • pp.129-136
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    • 2018
  • An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

  • Yin, Zhonggang;Li, Guoyin;Du, Chao;Sun, Xiangdong;Liu, Jing;Zhong, Yanru
    • Journal of Power Electronics
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    • v.17 no.1
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    • pp.149-160
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    • 2017
  • To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

Estimation of ESR in the DC-Link Capacitors of AC Motor Drive Systems with a Front-End Diode Rectifier

  • Nguyen, Thanh Hai;Le, Quoc Anh;Lee, Dong-Choon
    • Journal of Power Electronics
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    • v.15 no.2
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    • pp.411-418
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    • 2015
  • In this paper, a new method for the online estimation of equivalent series resistances (ESR) of the DC-link capacitors in induction machine (IM) drive systems with a front-end diode rectifier is proposed, where the ESR estimation is conducted during the regenerative operating mode of the induction machine. In the first place, a regulated AC current component is injected into the q-axis current component of the induction machine, which induces the current and voltage ripple components in the DC-link. By processing these AC signals through digital filters, the ESR can be estimated by a recursive least squares (RLS) algorithm. To acquire the AC voltage across the ESR, the DC-link voltage needs to be measured at a double sampling frequency. In addition, the ESR current is simply reconstructed from the stator currents and switching states of the inverter. Experimental results have shown that the estimation error of the ESR is about 1.2%, which is quite acceptable for condition monitoring of the capacitor.

Non-intrusive Calibration for User Interaction based Gaze Estimation (사용자 상호작용 기반의 시선 검출을 위한 비강압식 캘리브레이션)

  • Lee, Tae-Gyun;Yoo, Jang-Hee
    • Journal of Software Assessment and Valuation
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    • v.16 no.1
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    • pp.45-53
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    • 2020
  • In this paper, we describe a new method for acquiring calibration data using a user interaction process, which occurs continuously during web browsing in gaze estimation, and for performing calibration naturally while estimating the user's gaze. The proposed non-intrusive calibration is a tuning process over the pre-trained gaze estimation model to adapt to a new user using the obtained data. To achieve this, a generalized CNN model for estimating gaze is trained, then the non-intrusive calibration is employed to adapt quickly to new users through online learning. In experiments, the gaze estimation model is calibrated with a combination of various user interactions to compare the performance, and improved accuracy is achieved compared to existing methods.

A Path Analytic Exploration of Consumer Information Search in Online Clothing Purchases (온라인 의복구매를 위한 소비자 정보탐색의 경로분석적 탐구)

  • Kim, Eun-Young;Knight, Dee K.
    • Journal of the Korean Society of Clothing and Textiles
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    • v.31 no.12
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    • pp.1721-1732
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    • 2007
  • This study identified types of information source, and explored a path model for consumer information search by shopping attributes in the context of online decision making. Participants completed self-administered questionnaires during regularly scheduled classes. A total of 219 usable questionnaires were obtained from respondents who enroll at universities in the southwestern region of the United States. For data analysis, factor analysis and path model estimation were used. Consumer information source was classified into three types for online clothing purchases: Online source, Offline retail source, and Mass media. Consumers were more likely to rely on offline retail source for online clothing purchases, than other sources. In consumer information search by shopping attributes, online sources were more likely to be related to transaction-related attributes(e.g., incentive service), whereas offline retail source(e.g., displays in stores, manufacturer's catalogs and pamphlets) were more likely to be related to product and market related attributes(e.g., aesthetics, price) when purchasing clothing online. Also, the path model emphasizes the effect of shopping attributes on traditional retailer search behavior, leading to online purchase intention for clothing. This study supports consumer information search by attributes, and discusses a managerial implication of multi-channel retailing for apparel.

Gradient Estimation for Progressive Photon Mapping (점진적 광자 매핑을 위한 기울기 계산 기법)

  • Donghee Jeon;Jeongmin Gu;Bochang Moon
    • Journal of the Korea Computer Graphics Society
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    • v.30 no.3
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    • pp.141-147
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    • 2024
  • Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons progressively generated from lights. Its hyperparameter, which controls the reduction rate of the density estimation, highly affects the quality of its rendering image due to the bias-variance tradeoff of pixel estimates in photon-mapped results. We can minimize the errors of rendered pixel estimates in progressive photon mapping by estimating the optimal parameters based on gradient-based optimization techniques. To this end, we derived the gradients of pixel estimates with respect to the parameters when performing progressive photon mapping and compared our estimated gradients with finite differences to verify estimated gradients. The gradient estimated in this paper can be applied in an online learning algorithm that simultaneously performs progressive photon mapping and parameter optimization in future work.

Copyright Royalty Regulation and Competition in the Music Retail Market

  • YANG, YONG HYEON
    • KDI Journal of Economic Policy
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    • v.39 no.1
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    • pp.83-102
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    • 2017
  • Price control can restore efficiency in some cases, but an uncarefully designed policy fails to restore efficiency, yields side effects, or even exacerbates efficiency losses. This paper shows that the copyright royalty rule, which takes the greater of ad valorem royalties and perunit royalties, tends to fix the prices of final goods at a specific level. Such a rule weakens competition as it prevents prices from decreasing even when market conditions change, having negative effects on social welfare as well as consumer surplus. Counterfactual analyses using estimation results in the Korean online music service industry show that firms could have profitably reduced prices if the ad valorem rule had been applied instead, although they did not have an incentive to do so under the original combination rule.