• 제목/요약/키워드: multiple methods combination

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A comparison of multiple hypothesis testing methods and combination methods in seamless Phase II/III clinical trials (심리스 제2상/제3상 임상시험에서 다중가설검정방법과 결합검정방법의 비교연구)

  • Han, Song;Yoo, Hanna;Lee, Jae Won
    • The Korean Journal of Applied Statistics
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    • v.32 no.1
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    • pp.1-13
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    • 2019
  • An adaptive seamless Phase II/III clinical trial design enables a reduction in the sample size (in comparison to a conventional design) that also shortens the clinical development time. It is also very effective in clinical trials since it can have higher statistical power than Phase III alone. In this study, we use extensive simulation studies to compare several multiple hypothesis testing methods that can help select the best doses in a Phase II study along with several methods to combine p-values of the Phase II and Phase III study.

A Hybrid Fault Diagnosis Method based on SDG and PLS;Tennessee Eastman Challenge Process

  • Lee, Gi-Baek
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.110-115
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    • 2004
  • The hybrid fault diagnosis method based on a combination of the signed digraph (SDG) and the partial least-squares (PLS) has the advantage of improving the diagnosis resolution, accuracy and reliability, compared to those of previous qualitative methods, and of enhancing the ability to diagnose multiple fault. In this study, the method is applied for the multiple fault diagnosis of the Tennessee Eastman challenge process, which is a realistic industrial process for evaluating process contol and monitoring methods. The process is decomposed using the local qualitative relationships of each measured variable. Dynamic PLS (DPLS) model is built to estimate each measured variable, which is then compared with the estimated value in order to diagnose the fault. Through case studies of 15 single faults and 44 double faults, the proposed method demonstrated a good diagnosis capability compared with previous statistical methods.

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Motion estimation method using multiple linear regression model (다중선형회귀모델을 이용한 움직임 추정방법)

  • 김학수;임원택;이재철;이규원;박규택
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.34S no.10
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    • pp.98-103
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    • 1997
  • Given the small bit allocation for motion information in very low bit-rate coding, motion estimation using the block matching algorithm(BMA) fails to maintain an acceptable level of prediction errors. The reson is that the motion model, or spatial transformation, assumed in block matching cannot approximate the motion in the real world precisely with a small number of parameters. In order to overcome the drawback of the conventional block matching algorithm, several triangle-based methods which utilize triangular patches insead of blocks have been proposed. To estimate the motions of image sequences, these methods usually have been based on the combination of optical flow equation, affine transform, and iteration. But the compuataional cost of these methods is expensive. This paper presents a fast motion estimation algorithm using a multiple linear regression model to solve the defects of the BMA and the triange-based methods. After describing the basic 2-D triangle-based method, the details of the proposed multiple linear regression model are presented along with the motion estimation results from one standard video sequence, representative of MPEG-4 class A data. The simulationresuls show that in the proposed method, the average PSNR is improved about 1.24 dB in comparison with the BMA method, and the computational cost is reduced about 25% in comparison with the 2-D triangle-based method.

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Characterization of Korean Porcelainsherds by Neutron Activation Analysis

  • Lee, Chul;Kang, Hyung-Tae;Kim, Seung-Won
    • Bulletin of the Korean Chemical Society
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    • v.9 no.4
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    • pp.223-231
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    • 1988
  • Some pattern recognition methods have been used to characterize Korean ancient porcelainsherds using their elemental composition as analyzed by instrumental neutron activation analysis. A combination of analytical data by means of statistical linear discriminant analysis(SLDA) has resulted in removal of redundant variables, optimal linear combination of meaningful variables and formulation of classification rules. The plot in the first-to-second discriminant scores has shown that the three distinct territorial regions exist among porcelainsherds of Kyungki, Chunbuk-Chungnam, and Chunnam, with respective efficiencies of 20/30, 22/27 and 14/15. Similar regions have been found to exist among punchong porcelain and ceradonsherds of Kyungki, Chungnam and Chunbuk, with respective efficiencies of 7/9, 15/16 and 6/6. Classification has been further attempted by statistical isolinear multiple component analysis(SIMCA), using the sample set selected appropriately through SLDA as training set. For this purpose, all analytical data have been used. An agreement has generally been found between two methods, i.e., SLDA and SIMCA.

Incremental Antenna Selection Based on Lattice-Reduction for Spatial Multiplexing MIMO Systems

  • Kim, Sangchoon
    • Journal of Advanced Information Technology and Convergence
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    • v.10 no.1
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    • pp.1-14
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    • 2020
  • Antenna selection is a method to enhance the performance of spatial multiplexing multiple-input multiple-output (MIMO) systems, which can achieve the diversity order of the full MIMO systems. Although various selection criteria have been studied in the literature, they should be adjusted to the detection operation implemented at the receiver. In this paper, antenna selection methods that optimize the post-processing signal-to-noise ratio (SNR) and eigenvalue are considered for the lattice reduction (LR)-based receiver. To develop a complexity-efficient antenna selection algorithm, the incremental selection strategy is adopted. Moreover, for improvement of performance, an additional iterative selection method is presented in combination with an incremental strategy.

A Combination Method of Unconstrained Handwritten Numerals Recognizers Using Strutural Feature Analyzer (구조적 특징 분석기를 이용한 무제약 필기 숫자 인식기의 결합)

  • Kim, Won-Woo;Paik, Jong-Hyun;Lee, Kwan-Yong;Byun, Hye-Ran;Lee, Yill-Byung
    • Korean Journal of Cognitive Science
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    • v.7 no.1
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    • pp.37-56
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    • 1996
  • In this paper,we design a verifier for unconstrained handwritten numerals using structural feature analysis,and use it as a comnination algorithm for multiple recognizers.The existing combination algorithms mainly use learnings,statistical methods,or probabilistic methods without considering structural features of numerals.That is why they cannot recognize some numerals which human can identify clearly.To overcome the shortcomings,we design one-to-one verifiers which compare and analyze the relative structural features between frequently confused numeral pairs,and apply them to combine multiple recongnizers.Structural features for verification consist of contour,direction al chain code,polygonal approximation,and zero crossing number of horizontal/vertical projections. We gained a 97.95% reliability with CENPARMI numeral data,and showed that some misconceived factors generated from typical combination algorithms can be removed.

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Multi-Label Combination for Prediction of Protein Subcellular Localization (다중레이블 조합을 사용한 단백질 세포내 위치 예측)

  • Chi, Sang-Mun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.7
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    • pp.1749-1756
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    • 2014
  • Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.

Multimodal Sentiment Analysis for Investigating User Satisfaction

  • Hwang, Gyo Yeob;Song, Zi Han;Park, Byung Kwon
    • The Journal of Information Systems
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    • v.32 no.3
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    • pp.1-17
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    • 2023
  • Purpose The proliferation of data on the internet has created a need for innovative methods to analyze user satisfaction data. Traditional survey methods are becoming inadequate in dealing with the increasing volume and diversity of data, and new methods using unstructured internet data are being explored. While numerous comment-based user satisfaction studies have been conducted, only a few have explored user satisfaction through video and audio data. Multimodal sentiment analysis, which integrates multiple modalities, has gained attention due to its high accuracy and broad applicability. Design/methodology/approach This study uses multimodal sentiment analysis to analyze user satisfaction of iPhone and Samsung products through online videos. The research reveals that the combination model integrating multiple data sources showed the most superior performance. Findings The findings also indicate that price is a crucial factor influencing user satisfaction, and users tend to exhibit more positive emotions when content with a product's price. The study highlights the importance of considering multiple factors when evaluating user satisfaction and provides valuable insights into the effectiveness of different data sources for sentiment analysis of product reviews.

The experimental bias in person perception as results of presentation method of stimulus (자극물의 제시방법에 따른 대인지각에서의 편파)

  • 김재숙;김희숙
    • Journal of the Korean Society of Clothing and Textiles
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    • v.27 no.5
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    • pp.496-504
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    • 2003
  • The purpose of this study was (1) to identify the experimental bias which could appear person perception as results of presentation methods (2) to find out the most desirable method in presentation of stimulus. The research was a quasi experiment and the subjects were 773 male and female undergraduate students. The experimental instruments consisted of a set of stimulus and semantic differential scales of 7- point hi-polar adjectives. The collected data were analyzed by Factor Analysis, ANOVA(analysis of variance), Scheffe's multiple range test. The independent variables were presentation orders and presentation time of stimulus. The results were as follows: First, five factors which were potency, sociality, appearance, evaluation, activity impression dimensions emerged to account for the methods of development of stimulus. Second, the presentation order of stimulus in the combination of four stimuli sets had significant effects on the 3 impressional factors(sociality, appearance, evaluation). The presentation order of stimuli in the combination of eight stimuli set had significant effects on the 3 impressional factors(potency, sociality, appearance) and the presentation order of stimuli in the combination of eight stimuli set showed a significant effect on memorization of stimulus and the results support the recency effect. Third, the presentation time of stimuli had significant effects on the 2 impressional factors(potency, activity). 30 seconds presentation as well as free duration time resulted less experimental bias than 3 seconds presentation.

A Novel Decoding Scheme for MIMO Signals Using Combined Depth- and Breadth-First Search and Tree Partitioning (깊이 우선과 너비 우선 탐색 기법의 결합과 트리 분할을 이용한 다중 입출력 신호를 위한 새로운 최우도 복호 기법)

  • Lee, Myung-Soo;Lee, Young-Po;Song, Iick-Ho;Yoon, Seok-Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.1C
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    • pp.37-47
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    • 2011
  • In this paper, we propose a novel ML decoding scheme based on the combination of depth- and breadth-first search methods on a partitioned tree for multiple input multiple output systems. The proposed scheme first partitions the searching tree into several stages, each of which is then searched by a depth- or breadth-first search method, possibly exploiting the advantages of both the depth- and breadth-first search methods in an organized way. Numerical results indicate that, when the depth- and breadth-first search algorithms are adopted appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.