• Title/Summary/Keyword: convergence rates

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A Study on Bandwith Selection Based on ASE for Nonparametric Regression Estimator

  • Kim, Tae-Yoon
    • Journal of the Korean Statistical Society
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    • v.30 no.1
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    • pp.21-30
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    • 2001
  • Suppose we observe a set of data (X$_1$,Y$_1$(, …, (X$_{n}$,Y$_{n}$) and use the Nadaraya-Watson regression estimator to estimate m(x)=E(Y│X=x). in this article bandwidth selection problem for the Nadaraya-Watson regression estimator is investigated. In particular cross validation method based on average square error(ASE) is considered. Theoretical results here include a central limit theorem that quantifies convergence rates of the bandwidth selector.tor.

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A neural network algorithm for the channel assignment in cellular mobile communication (이동통신에서의 채널할당 신경망 알고리즘)

  • 최광호;이강장;김준한;전옥준;조용범
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.35C no.5
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    • pp.59-68
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    • 1998
  • This paper proposes a neural network algorithm for a channel assignment in cellular mobile communications. The proposed algorithm is developed base on hopfield neural network in order to minimize the number of channel without a confliction between cells. To compare the performance of the proposed algorithm, we used seven benchmark problems selected from kunz's and funabiki's papers. Experimental results show that the convergence times are reduced form 27% to 66% compared with Kunz's and funabiki's algorithm and vonvergence rates are improved to 100%.

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An Efficient Multigrid Diagonalized ADI Method for 3-Dimensional Compressible Flow Analysis (3차원 압축성 유동 해석을 위한 효율적인 다중 격자 DADI 기법)

  • Park Soo-Hyung;Sung Chun-ho;Kwon Jang Hyuk
    • 한국전산유체공학회:학술대회논문집
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    • 1998.05a
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    • pp.29-34
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    • 1998
  • An efficient 3-dimensional compressible solver is developed using the second-order upwind TVD scheme and the multigrid diagonalized ADI method. The multigrid method is improved so that the present DADI algorithm obtains better convergence rates. Results are computed on Cray C90 computer for transonic unsaperated flows past ONERA-M6 wing to demonstrate the accuracy and efficiency. The results show good agreement with experimetal data. A reduction of four orders of residual for 3-dimensional transonic flow is obtained about 99 seconds.

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Bootstrap Confidence Intervals for a One Parameter Model using Multinomial Sampling

  • Jeong, Hyeong-Chul;Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.2
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    • pp.465-472
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    • 1999
  • We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

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An Optical Implementation of Associative Memory Based on Inner Product Neural Network Model

  • Gil, S.K.
    • Proceedings of the Optical Society of Korea Conference
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    • 1989.02a
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    • pp.89-94
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    • 1989
  • In this paper, we propose a hybrid optical/digital version of the associative memory which improve hardware efficiency and increase convergence rates. Multifocus hololens are used as space-varient optical element for performing inner product and summation function. The real-time input and the stored states of memory matrix is formated using LCTV. One method of adaptively changing the weights of stored vectors during each iteration is implemented electronically. A design for a optical implementation scheme is discussed and the proposed architecture is demonstrated the ability of retrieving with computer simmulation.

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Improved Rate of Convergence in Kohonen Network using Dynamic Gaussian Function (동적 가우시안 함수를 이용한 Kohonen 네트워크 수렴속도 개선)

  • Kil, Min-Wook;Lee, Geuk
    • Journal of the Korea Society of Computer and Information
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    • v.7 no.4
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    • pp.204-210
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    • 2002
  • The self-organizing feature map of Kohonen has disadvantage that needs too much input patterns in order to converge into the equilibrium state when it trains. In this paper we proposed the method of improving the convergence speed and rate of self-organizing feature map converting the interaction set into Dynamic Gaussian function. The proposed method Provides us with dynamic Properties that the deviation and width of Gaussian function used as an interaction function are narrowed in proportion to learning times and learning rates that varies according to topological position from the winner neuron. In this Paper. we proposed the method of improving the convergence rate and the degree of self-organizing feature map.

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Investigation of phenol phormaldehyde-based photoresist at an initial stage of destruction in $O_2$ and $N_2O$ radiofrequency discharges

  • Shutov, D.A.;Kang, Seung-Youl;Baek, Kyu-Ha;Suh, Kyung-Soo;Min, Nam-Ki;Kwon, Kwang-Ho
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2007.11a
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    • pp.214-215
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    • 2007
  • Etch rates and surface chemistry of phenol formaldehyde-based photoresist after short time $O_2\;and\;N_2O$ radio frequency (RF) plasma treatment depending on exposure time were investigated. It was found that the etch rate of photoresist sharply increased after discharge turn on and reached a limit with increase in plasma exposure time in both gases. X-ray photoelectron spectroscopy (XPS) analysis showed that the surface chemical structure become nearly constant after the treatment of 15 sec. Concentration of surface oxygen-containing groups after processing both in oxygen and in $N_2O$ plasmas is similar.

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Efficient Resource Slicing Scheme for Optimizing Federated Learning Communications in Software-Defined IoT Networks

  • Tam, Prohim;Math, Sa;Kim, Seokhoon
    • Journal of Internet Computing and Services
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    • v.22 no.5
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    • pp.27-33
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    • 2021
  • With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

Development of Real-Time Objects Segmentation for Dual-Camera Synthesis in iOS (iOS 기반 실시간 객체 분리 및 듀얼 카메라 합성 개발)

  • Jang, Yoo-jin;Kim, Ji-yeong;Lee, Ju-hyun;Hwang, Jun
    • Journal of Internet Computing and Services
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    • v.22 no.3
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    • pp.37-43
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    • 2021
  • In this paper, we study how objects from front and back cameras can be recognized in real time in a mobile environment to segment regions of object pixels and synthesize them through image processing. To this work, we applied DeepLabV3 machine learning model to dual cameras provided by Apple's iOS. We also propose methods using Core Image and Core Graphics libraries from Apple for image synthesis and postprocessing. Furthermore, we improved CPU usage than previous works and compared the throughput rates and results of Depth and DeepLabV3. Finally, We also developed a camera application using these two methods.

Beam Tracking Method Using Unscented Kalman Filter for UAV-Enabled NR MIMO-OFDM System with Hybrid Beamforming

  • Yuna, Sim;Seungseok, Sin;Jihun, Cho;Sangmi, Moon;Young-Hwan, You;Cheol Hong, Kim;Intae, Hwang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.1
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    • pp.280-294
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    • 2023
  • Unmanned aerial vehicles (UAVs) and millimeter-wave frequencies play key roles in supporting 5G wireless communication systems. They expand the field of wireless communication by increasing the data capacities of communication systems and supporting high data rates. However, short wavelengths, owing to the high millimeter-wave frequencies can cause problems, such as signal attenuation and path loss. To address these limitations, research on high directional beamforming technologies continue to garner interest. Furthermore, owing to the mobility of the UAVs, it is essential to track the beam angle accurately to obtain full beamforming gain. This study presents a beam tracking method based on the unscented Kalman filter using hybrid beamforming. The simulation results reveal that the proposed beam tracking scheme improves the overall performance in terms of the mean-squared error and spectral efficiency. In addition, by expanding analog beamforming to hybrid beamforming, the proposed algorithm can be used even in multi-user and multi-stream environments to increase data capacity, thereby increasing utilization in new-radio multiple-input multiple-output orthogonal frequency-division multiplexing systems.