• Title/Summary/Keyword: Weighted combination

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Efficient routing in multicast mesh by using forwarding nodes and weighted cost function

  • Vyas, Kapila;Khuteta, Ajay;Chaturvedi, Amit
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.12
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    • pp.5928-5947
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    • 2019
  • Multicast Mesh based Mobile Ad-hoc NETworks (MANETs) provide efficient data transmission in energy restraint areas without a fixed infrastructure. In this paper, the authors present an improved version of protocol SLIMMER developed by them earlier, and name it SLIMMER-SN. Most mesh-based protocols suffer from redundancy; however, the proposed protocol controls redundancy through the concept of forwarding nodes. The proposed protocol uses remaining energy of a node to decide its energy efficiency. For measuring stability, a new metric called Stability of Node (SN) has been introduced which depends on transmission range, node density and node velocity. For data transfer, a weighted cost function selects the most energy efficient nodes / most stable nodes or a weighted combination of both. This makes the node selection criteria more dynamic. The protocol works in two steps: (1) calculating SN and (2) using SN value in the weighted cost function for selection of nodes. The study compared the proposed protocol, with other mesh-based protocols PUMA and SLIMMER, based on packet delivery ratio (PDR), throughput, end-to-end delay and average energy consumption under different simulation conditions. Results clearly demonstrate that SLIMMER-SN outperformed both PUMA and SLIMMER.

Data Department Linear Combination of Weighted Order Statistics(DD-LWOS) Filtering Based on Local Statistics (국부 통계를 기반으로 한 가중차수 통계의 데이터 의존 선형조합 필터링(DD-LWOS))

  • 박동희;배철수
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.4
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    • pp.639-644
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    • 2002
  • Nonlinear filters which are utilized rank-order information and temporal-order information, have many proposed, in order to restore nonstationary signals which are corrupted by additive noise. In this paper, we propose a data-dependent LWOS filter whose coefficients change based on local statistics. LWOS(Linear Combination of Weighted Order Statistics) filters[1]which also utilized two informations, and have properties of efficient impulsive and nonimpulsive noise attenuation and sufficiently details and edges preservation. DD-LWOS filters can remove non-impulsive oises while preserving signal details. DD-LWOS2 filter gets more better performance than DD-LWOS filter when input image corrupted by additive noise which includes Impulsive noise components.

Hue-based Noise-tolerant Corner Detector Robust to Shadows (그림자에 강건한 색상 기반 내잡음성 코너 검출자)

  • 박기현;박은진;최흥문
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.239-245
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    • 2004
  • A hue-based noise-tolerant corner detector is proposed for the exact detection of the real corners in spite of the shadows and random noise. Based on the fact that the hue gradient at the border of the opaque objects' shadow is smaller than the intensity gradient in HSI (hue-saturation-intensity) color space, the effects of shadow are eliminated by introducing the hue-weighted combination of vector gradient to the proposed corner detector. Furthermore, the proposed corner detector is robust to random noise by offsetting the contribution to the corner candidate when the polarities of the color gradients of the pixel pairs are out of phase each other. Results of the experiment show that the proposed corner detector can effectively detect the real corners.

Adaptive Residual DPCM using Weighted Linear Combination of Adjacent Residues in Screen Content Video Coding (스크린 콘텐츠 비디오의 압축을 위한 인접 화소의 가중 합을 이용한 적응적 Residual DPCM 기법)

  • Kang, Je-Won
    • Journal of Broadcast Engineering
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    • v.20 no.5
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    • pp.782-785
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    • 2015
  • In this paper, we propose a novel residual differential pulse-code modulation (RDPCM) coding technique to improve coding efficiency of screen content videos. The proposed method uses a weighted combination of adjacent residues to provide an accurate estimate in RDPCM. The weights are trained in previously coded samples by using an L1 optimization problem with the least absolute shrinkage and selection operation (LASSO). The proposed method achieves BD-rate saving about 3.1% in all-intra coding.

Developing a Method to Define Mountain Search Priority Areas Based on Behavioral Characteristics of Missing Persons

  • Yoo, Ho Jin;Lee, Jiyeong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.37 no.5
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    • pp.293-302
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    • 2019
  • In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

Differentiation between Glioblastoma and Solitary Metastasis: Morphologic Assessment by Conventional Brain MR Imaging and Diffusion-Weighted Imaging

  • Jung, Bo Young;Lee, Eun Ja;Bae, Jong Myon;Choi, Young Jae;Lee, Eun Kyoung;Kim, Dae Bong
    • Investigative Magnetic Resonance Imaging
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    • v.25 no.1
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    • pp.23-34
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    • 2021
  • Purpose: Differentiating between glioblastoma and solitary metastasis is very important for the planning of further workup and treatment. We assessed the ability of various morphological parameters using conventional MRI and diffusion-based techniques to distinguish between glioblastomas and solitary metastases in tumoral and peritumoral regions. Materials and Methods: We included 38 patients with solitary brain tumors (21 glioblastomas, 17 solitary metastases). To find out if there were differences in the morphologic parameters of enhancing tumors, we analyzed their shape, margins, and enhancement patterns on postcontrast T1-weighted images. During analyses of peritumoral regions, we assessed the extent of peritumoral non-enhancing lesion on T2- and postcontrast T1-weighted images. We also aimed to detect peritumoral neoplastic cell infiltration by visual assessment of T2-weighted and diffusion-based images, including DWI, ADC maps, and exponential DWI, and evaluated which sequence depicted peritumoral neoplastic cell infiltration most clearly. Results: The shapes, margins, and enhancement patterns of tumors all significantly differentiated glioblastomas from metastases. Glioblastomas had an irregular shape, ill-defined margins, and a heterogeneous enhancement pattern; on the other hand, metastases had an ovoid or round shape, well-defined margins, and homogeneous enhancement. Metastases had significantly more extensive peritumoral T2 high signal intensity than glioblastomas had. In visual assessment of peritumoral neoplastic cell infiltration using T2-weighted and diffusion-based images, all sequences differed significantly between the two groups. Exponential DWI had the highest sensitivity for the diagnosis of both glioblastoma (100%) and metastasis (70.6%). A combination of exponential DWI and ADC maps was optimal for the depiction of peritumoral neoplastic cell infiltration in glioblastoma. Conclusion: In the differentiation of glioblastoma from solitary metastatic lesions, visual morphologic assessment of tumoral and peritumoral regions using conventional MRI and diffusion-based techniques can also offer diagnostic information.

A Study on a Robust Clustered Group Multicast in Ad-hoc Networks (에드-혹 네트워크에서 신뢰성 있는 클러스터 기반 그룹 멀티캐스트 방식에 관한 연구)

  • Park, Yang-Jae;Lee, Jeong-Hyun
    • The KIPS Transactions:PartC
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    • v.10C no.2
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    • pp.163-170
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    • 2003
  • In this paper we propose a robust clustered croup Multicast in Ad-hoc network. The proposed scheme applies to weighted clustered Algorithm. Ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration or reliable support services such as wired network and base station. In ad hoc network routing protocol because of limited bandwidth and high mobility robust, simple and energy consume minimal. WCGM method uses a base structure founded on combination weighted value and applies combination weight value to cluster header keeping data transmission by scoped flooding, which is the advantage of the exiting FGMP method. Because this method has safe and reliable data transmission, it shows the effect to decrease both overhead to preserve transmission structure and overhead for data transmission.

A Study on Classification Performance Analysis of Convolutional Neural Network using Ensemble Learning Algorithm (앙상블 학습 알고리즘을 이용한 컨벌루션 신경망의 분류 성능 분석에 관한 연구)

  • Park, Sung-Wook;Kim, Jong-Chan;Kim, Do-Yeon
    • Journal of Korea Multimedia Society
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    • v.22 no.6
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    • pp.665-675
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    • 2019
  • In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.

Equivalence study of canonical correspondence analysis by weighted principal component analysis and canonical correspondence analysis by Gaussian response model (가중주성분분석을 활용한 정준대응분석과 가우시안 반응 모형에 의한 정준대응분석의 동일성 연구)

  • Jeong, Hyeong Chul
    • The Korean Journal of Applied Statistics
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    • v.34 no.6
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    • pp.945-956
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    • 2021
  • In this study, we considered the algorithm of Legendre and Legendre (2012), which derives canonical correspondence analysis from weighted principal component analysis. And, it was proved that the canonical correspondence analysis based on the weighted principal component analysis is exactly the same as Ter Braak's (1986) canonical correspondence analysis based on the Gaussian response model. Ter Braak (1986)'s canonical correspondence analysis derived from a Gaussian response curve that can explain the abundance of species in ecology well uses the basic assumption of the species packing model and then conducts generalized linear model and canonical correlation analysis. It is derived by way of binding. However, the algorithm of Legendre and Legendre (2012) is calculated in a method quite similar to Benzecri's correspondence analysis without such assumptions. Therefore, if canonical correspondence analysis based on weighted principal component analysis is used, it is possible to have some flexibility in using the results. In conclusion, this study shows that the two methods starting from different models have the same site scores, species scores, and species-environment correlations.

Generalized Weighted Linear Models Based on Distribution Functions - A Frequentist Perspective (분포함수를 기초로 일반화가중선형모형)

  • 여인권
    • The Korean Journal of Applied Statistics
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    • v.17 no.3
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    • pp.489-498
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    • 2004
  • In this paper, a new form of linear models referred to as generalized weighted linear models is proposed. The proposed models assume that the relationship between the response variable and explanatory variables can be modelled by a distribution function of the response mean and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models in which the parameter space may not be consistent with the space derived from linear predictors. The maximum likelihood estimation with Lagrange's undetermined multipliers is used to estimate the parameters and resampling method is applied to compute confidence intervals and to test hypotheses.