• 제목/요약/키워드: Sequential convergence

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THE SEQUENTIAL UNIFORM LAW OF LARGE NUMBERS

  • Bae, Jong-Sig;Kim, Sung-Yeun
    • 대한수학회보
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    • 제43권3호
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    • pp.479-486
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    • 2006
  • Let $Z_n(s,\;f)=n^{-1}\;{\sum}^{ns}_{i=1}(f(X_i)-Pf)$ be the sequential empirical process based on the independent and identically distributed random variables. We prove that convergence problems of $sup_{(s,\;f)}|Z_n(s,\;f)|$ to zero boil down to those of $sup_f|Z_n(1,\;f)|$. We employ Ottaviani's inequality and the complete convergence to establish, under bracketing entropy with the second moment, the almost sure convergence of $sup_{(s,\;f)}|Z_n(s,\;f)|$ to zero.

SEQUENTIAL COMPACTNESS AND SEMICOMPACTNESS

  • Myung, Jae Deuk;Choi, Hee Chan
    • Korean Journal of Mathematics
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    • 제5권2호
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    • pp.211-215
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    • 1997
  • In this paper, we introduce two notions of compactness defined by sequential convergence and compare them.

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A Study on Counter Design using Sequential Systems based on Synchronous Techniques

  • Park, Chun-Myoung
    • Journal of information and communication convergence engineering
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    • 제8권4호
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    • pp.421-426
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    • 2010
  • This paper presents a method of design the counter using sequential system based on synchronous techniques. For the design the counter, first of all, we derive switching algebras and their operations. Also, we obtain the next-state functions, flip-flop excitations and their input functions from the flip-flop. Then, we propose the algorithm which is a method of implementation of the synchronous sequential digital logic circuits. Finally, we apply proposed the sequential logic based on synchronous techniques to counter.

Acute Leukemia Classification Using Sequential Neural Network Classifier in Clinical Decision Support System

  • Ivan Vincent;Thanh.T.T.P;Suk-Hwan Lee;Ki-Ryong Kwon
    • International Journal of Computer Science & Network Security
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    • 제24권9호
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    • pp.97-104
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    • 2024
  • Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only covers the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.

Noise Reduction of Image Using Sequential Method of Cellular Automata

  • Kim, Tai-Suk;Lee, Seok-Ki
    • Journal of information and communication convergence engineering
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    • 제9권2호
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    • pp.224-229
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    • 2011
  • Cellular Automata is a discrete dynamical system that can be completely described in terms of local relation. For any given image, the system can save its features as well as increase or decrease the brightness of it locally through consideration of optimized transition in succession. These transitions in succession satisfy the function "Lyapunov" and have sequential movements. This study suggests the way of noise reduction for each image with the use of the Sequential Cellular Automata system. The mentioned transition in succession gives stable results with high-convergence performance to random noises and PSNR (Peak Signal-to-Noise Ratio) using histograms and MSE (Mean Square Error) for verification of effectiveness.

크리깅 모델을 이용한 순차적 근사최적화 (Sequential Approximate Optimization Using Kriging Metamodels)

  • 신용식;이용빈;류제선;최동훈
    • 대한기계학회논문집A
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    • 제29권9호
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    • pp.1199-1208
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    • 2005
  • Nowadays, it is performed actively to optimize by using an approximate model. This is called the approximate optimization. In addition, the sequential approximate optimization (SAO) is the repetitive method to find an optimum by considering the convergence of an approximate optimum. In some recent studies, it is proposed to increase the fidelity of approximate models by applying the sequential sampling. However, because the accuracy and efficiency of an approximate model is directly connected with the design area and the termination criteria are not clear, sequential sampling method has the disadvantages that could support an unreasonable approximate optimum. In this study, the SAO is executed by using trust region, Kriging model and Optimal Latin Hypercube design (OLHD). Trust region is used to guarantee the convergence and Kriging model and OLHD are suitable for computer experiment. finally, this SAO method is applied to various optimization problems of highly nonlinear mathematical functions. As a result, each approximate optimum is acquired and the accuracy and efficiency of this method is verified by comparing with the result by established method.