• Title/Summary/Keyword: adaptive weighting

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Locating the damaged storey of a building using distance measures of low-order AR models

  • Xing, Zhenhua;Mita, Akira
    • Smart Structures and Systems
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    • v.6 no.9
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    • pp.991-1005
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    • 2010
  • The key to detecting damage to civil engineering structures is to find an effective damage indicator. The damage indicator should promptly reveal the location of the damage and accurately identify the state of the structure. We propose to use the distance measures of low-order AR models as a novel damage indicator. The AR model has been applied to parameterize dynamical responses, typically the acceleration response. The premise of this approach is that the distance between the models, fitting the dynamical responses from damaged and undamaged structures, may be correlated with the information about the damage, including its location and severity. Distance measures have been widely used in speech recognition. However, they have rarely been applied to civil engineering structures. This research attempts to improve on the distance measures that have been studied so far. The effect of varying the data length, number of parameters, and other factors was carefully studied.

Balanced Information Potentials for PDF-Distance Algorithms with Constant Modulus Error

  • Kim, Namyong
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.4 no.4
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    • pp.295-299
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    • 2011
  • Blind equalization techniques have been widely used in wireless communication systems. In this paper, we propose to apply the balanced information potentials to the criterion of minimum Euclidian distance between two PDFs with constant modulus errors for adaptive blind equalizers. One of the two PDFs is constructed with constant modulus error samples and another does with Dirac delta functions. Two information potentials derived from the criterion are balanced in order to have better performance by putting a weighting factor to each information potentials. The proposed blind algorithm has shown in the MSE convergence performance that it can produce enhanced performance by over 3 dB of steady state MSE.

A Fuzzy Based Solution for Allocation and Sizing of Multiple Active Power Filters

  • Moradifar, Amir;Soleymanpour, Hassan Rezai
    • Journal of Power Electronics
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    • v.12 no.5
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    • pp.830-841
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    • 2012
  • Active power filters (APF) can be employed for harmonic compensation in power systems. In this paper, a fuzzy based method is proposed for identification of probable APF nodes of a radial distribution system. The modified adaptive particle swarm optimization (MAPSO) technique is used for final selection of the APFs size. A combination of Fuzzy-MAPSO method is implemented to determine the optimal allocation and size of APFs. New fuzzy membership functions are formulated where the harmonic current membership is an exponential function of the nodal injecting harmonic current. Harmonic voltage membership has been formulated as a function of the node harmonic voltage. The product operator shows better performance than the AND operator because all harmonics are considered in computing membership function. For evaluating the proposed method, it has been applied to the 5-bus and 18-bus test systems, respectively, which the results appear satisfactorily. The proposed membership functions are new at the APF placement problem so that weighting factors can be changed proportional to objective function.

Clustering with Adaptive weighting of Context-aware Linear regression (상황인식기반 선형회귀의 적응적 가중치를 적용한 클러스터링)

  • Lee, Kang-whan
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.271-273
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    • 2021
  • 본 논문은 이동노드의 클러스터링내에서 보다 효율적인클러스터링을 제공하고 유지하기위한 딥러닝의 선형회귀적 적응적 보정가중치에 따른 군집적 알고리즘을 제안한다. 대부분의 클러스터링 군집데이터를 처리함에 있어 상호관계에 따른 분류체계가 제공된다. 이러한 경우 이웃한 이동노드중 목적노드와는 연결가능성이 가장높은 이동노드를 클러스터내에서 중계노드로 선택해야 한다. 본 연구에서는 이러한 상황정보를 이해하고 동적이동노드간 속도와 방향속성정보간의 상관관계의 친밀도를 고려한 자율학습기반의 회귀적 모델에서 적응적 가중치에 따른 분류를 제시한다. 본 논문에서는 이러한 상황정보를 이해하고 클러스터링을 유지할 수 있는 자율학습기반의 적응적 가중치에 따른 딥러닝 모델을 제시 한다.

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Block-based Motion Vector Smoothing for Nonrigid Moving Objects (비정형성 등속운동 객체의 움직임 추정을 위한 블록기반 움직임 평활화)

  • Sohn, Young-Wook;Kang, Moon-Gi
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.44 no.6
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    • pp.47-53
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    • 2007
  • True motion estimation is necessary for deinterlacing, frame-rate conversion, and film judder compensation. There have been several block-based approaches to find true motion vectors by tracing minimum sum-of-absolute-difference (SAD) values by considering spatial and temporal consistency. However, the algorithms cannot find robust motion vectors when the texture of objects is changed. To find the robust motion vectors in the region, a recursive vector selection scheme and an adaptive weighting parameter are proposed. Previous frame vectors are recursively averaged to be utilized for motion error region. The weighting parameter controls fidelity to input vectors and the recursively averaged ones, where the input vectors come from the conventional estimators. If the input vectors are not reliable, then the mean vectors of the previous frame are used for temporal consistency. Experimental results show more robust motion vectors than those of the conventional methods in time-varying texture objects.

User Profile based Personalized Web Agent (사용자 프로파일 기반 개인 웹 에이전트)

  • So, Young-Jun;Park, Young-Tack
    • Journal of KIISE:Software and Applications
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    • v.27 no.3
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    • pp.248-256
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    • 2000
  • This paper presents a personalized web agent that constructs user profile which consists of user preferences on the web and recommends his/her relevant information to the user. The personalized web agent consists of monitor agent, user profile construction agent, and user profile refinement agent. The monitor agent makes a user describe his/her preferences directly and it creates the database of preference document, finally performs several keyword extraction to increase the accuracy of the DB. The user profile construction agent transforms the extracted keywords into user profile that could be confirmed and edited by the user. and the refinement agent refines user profile by recursively learning and processing user feedback. In this paper, we describe the several keyword weighting and inductive learning techniques in detail. Finally, we describe the adaptive web retrieval and push agent that perform adaptive services to the user.

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Partition and Caching Mechanism for GML Visualization on Mobile Device (모바일 디바이스에서 GML 가시화를 위한 분할 및 캐싱 기법)

  • Song, Eun-Ha;Park, Yong-Jin;Han, Won-Hee;Jeong, Young-Sik
    • Journal of Korea Multimedia Society
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    • v.11 no.7
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    • pp.1025-1034
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    • 2008
  • In this paper, we developed GridGML for efficiently supplying a GML and visualizing the map with partitioning map and caching method to a mobile device. In order to overcome the weighting of a file, which is the biggest weakness of a GML, GridGML extracts only the most necessary parts for the visualization of the map among GML attributes, and makes the file light as a class instance by applying an offset value. GridGML manages a partition based on the visualization area of a mobile device to visualize the map to a mobile device in real time, and transmits the partition area by serializing it for the benefit of transmission. Also, the received partition area is compounded in a mobile device and is visualized by being partitioned again as four visible areas based on the display of a mobile device. Then, the area is managed by applying a caching algorithm in consideration of repetitiveness for a received map for the efficient operation of resources. Also, in order to prevent the delay in transmission time as regards the instance density area of the map, an adaptive map partition mechanism is proposed for maintaining the transmission time uniformly.

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Real-time 14N NQR-based sodium nitrite analysis in a noisy field

  • Mohammad Saleh Sharifi;Ho Seung Song;Hossein Afarideh;Mitra Ghergherehchi;Mehdi Simiari
    • Nuclear Engineering and Technology
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    • v.55 no.12
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    • pp.4570-4575
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    • 2023
  • Noise and Radio-frequency interference or RFI causes a significant restriction on the Free induction Decay or FID signal detection of the Nuclear Quadrupole Resonance procedure. Therefore, using this method in non-isolated environments such as industry and ports requires extraordinary measures. For this purpose, noise reduction algorithms and increasing signal-to-noise-and-interference ratio or SNIR have been used. In this research, sodium nitrite has been used as a sample and algorithms have been tested in a non-isolated environment. The resonant frequencies for the 150 g of test sample were measured at 303 K at about 1 MHz and 3.4 MHz. The main novelty in this study was, (1) using two types of antennas in the receiver to improve adaptive noise and interference cancellation, (2) using a separate helical antenna in the transmitter to eliminate the duplexer, (3) estimating the noise before sending the pulse to calculate the weighting factors and reduce the noise by adaptive noise cancellation, (3) reject the interference by blanking algorithm, (4) pulse integration in the frequency domain to increase the SNR, and (5) increasing the detection speed by new pulse integration technique. By interference rejection and noise cancellation, the SNIR is improved to 9.24 dB at 1 MHz and to 7.28 dB at 3.4 MHz, and by pulse integration 44.8 dB FID signal amplification is achieved, and the FID signals are detected at 1.057 MHz and 3.402 MHz at room temperature.

Adaptive User Profile for Information Retrieval from the Web

  • Srinil, Phaitoon;Pinngern, Ouen
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1986-1989
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    • 2003
  • This paper proposes the information retrieval improvement for the Web using the structure and hyperlinks of HTML documents along with user profile. The method bases on the rationale that terms appearing in different structure of documents may have different significance in identifying the documents. The method partitions the occurrence of terms in a document collection into six classes according to the tags in which particular terms occurred (such as Title, H1-H6 and Anchor). We use genetic algorithm to determine class importance values and expand user query. We also use this value in similarity computation and update user profile. Then a genetic algorithm is used again to select some terms from user profile to expand the original query. Lastly, the search engine uses the expanded query for searching and the results of the search engine are scored by similarity values between each result and the user profile. Vector space model is used and the weighting schemes of traditional information retrieval were extended to include class importance values. The tested results show that precision is up to 81.5%.

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Dual EKF-Based State and Parameter Estimator for a LiFePO4 Battery Cell

  • Pavkovic, Danijel;Krznar, Matija;Komljenovic, Ante;Hrgetic, Mario;Zorc, Davor
    • Journal of Power Electronics
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    • v.17 no.2
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    • pp.398-410
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    • 2017
  • This work presents the design of a dual extended Kalman filter (EKF) as a state/parameter estimator suitable for adaptive state-of-charge (SoC) estimation of an automotive lithium-iron-phosphate ($LiFePO_4$) cell. The design of both estimators is based on an experimentally identified, lumped-parameter equivalent battery electrical circuit model. In the proposed estimation scheme, the parameter estimator has been used to adapt the SoC EKF-based estimator, which may be sensitive to nonlinear map errors of battery parameters. A suitable weighting scheme has also been proposed to achieve a smooth transition between the parameter estimator-based adaptation and internal model within the SoC estimator. The effectiveness of the proposed SoC and parameter estimators, as well as the combined dual estimator, has been verified through computer simulations on the developed battery model subject to New European Driving Cycle (NEDC) related operating regimes.