• 한국수학교육학회지시리즈A:수학교육
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• 제37권1호
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• pp.1-13
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• 1998

In this paper, we have represented the efficient way how to enumerate the optimal number-right scores to adjust the item difficulty and to improve item discrimination. To estimate the optimal number-right scores in two equivalent math-tests by linear score equating a measurement error model was applied to the true scores observed from a pair of equivalent math-tests assumed to measure same trait. The model specification for true scores which is represented by the bivariate model is a simple regression model to inference the optimal number-right scores and we assume again that the two simple regression lines of raw scores and true scores are independent each other in their error models. We enumerated the difference between mean value of $\chi$＊ and ${\mu}$$\_$$\chi$/ and the difference between the mean value of y＊and a＋b${\mu}$$\_$$\chi$/ by making an inference the estimates from 2 error variable regression model. Furthermore, so as to distinguish from the original score points, the estimated number-right scores y’$\^$*/ as the estimated regression values of true scores with the same coordinate were moved to center points that were composed of such difference values with result of such parallel score moving procedure as above mentioned. We got the asymptotically normal distribution in Figure 5 that was represented as the optimal distribution of the optimal number-right scores so that we could decide the optimal proportion of number-right score in each item. Also by assumption that equivalence of two tests is closely connected to unidimensionality of a student’s ability. we introduce new definition of trait score to evaluate such ability in each item. In this study there are much limitations in getting the real true scores and in analyzing data of the bivariate error model. However, even with these limitations we believe that this study indicates that the estimation of optimal number right scores by using this enumeration procedure could be easily achieved.

• ETRI Journal
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• 제36권4호
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• pp.599-608
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• 2014

This paper studies a parallel relay network where the relays employ an amplify-and-forward (AF) relaying scheme and are subjected to individual power constraints. We consider correlated effective relay noise arising from practical scenarios when the relays are exposed to common interferers. Assuming that the noise covariance and the full channel state information are available, we investigate the problem of finding the optimal AF scheme in terms of maximum end-to-end transmission rate. It is shown that the maximization problem can be equivalently transformed to a convex semi-definite program, which can be efficiently solved. Then an upper bound on the maximum achievable AF rate of this network is provided to further evaluate the performance of the optimal AF scheme. It is proved that the upper bound can be asymptotically achieved in two special regimes when the transmit power of the source node or the relays is sufficiently large. Finally, both theoretical and numerical results are given to show that, on average, noise correlation is beneficial to the transmission rate - whether the relays know the noise covariance matrix or not.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.251-260
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• 2009

이 논문에서는 단순 선형회귀 모형에서 회귀 계수의 최적 추정량을 구할 수 있는 자기공분산에 근거한 추정 방법을 제시하였다. 이 방법이 직관적으로 매혹적이지는 않지만 이 최적 추정량이 해당 회귀 계수의 불편추정량이 된다. 설명변수가 0과 1사이의 균등간격의 값을 가지면, 오차가 자기회귀 이동평균 모형을 따르면 성립하는 조건 하에서 이 최적 추정량이 최소제곱 추정량과 점근적으로 통일한 분포를 가진다는 것을 보였다. 추가적으로 똑같은 조건 하에서 이 최적 추정량이 해당 회귀 계수에 확률상 수렴한다는 것을 자체적으로 입증하였다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1991년도 추계학술대회
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• pp.44-47
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• 1991

There exists a transform trellis code that is optimal for stationary Gaussian sources and the squared-error distortion measure at all rates. In this paper, we train an asymptotically optimal version of such a code to obtain one which is matched better to the statistics of real world data. The training algorithm uses the M-algorithm to search the trellis codebook and the LBG-algorithm to update the trellis codebook. To adapt the codebook for the varying input data. we use two gain-adaptive methods. The gain-adaptive scheme 1, which normalizes input block data by its gain factor, is applied to images at rate 0.5 bits/pixel. When each block is encoded at the same rate, the nonstationarity among the block variances leads to a variation in the resulting distortion from one block to another. To alleviate the non-uniformity among the encoded image, we design four clusters from the block power, in which each cluster has its own trellis codebook and different rates. The rate of each cluster is assigned through requiring a constant distortion per-letter. This gain-adaptive scheme 2 produces good visual and measurable quality at low rates.

• Journal of Communications and Networks
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• 제15권1호
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• pp.102-110
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• 2013

This paper considers a cooperative orthogonal frequency division multiple access (OFDMA)-based cognitive radio network where the primary system leases some of its subchannels to the secondary system for a fraction of time in exchange for the secondary users (SUs) assisting the transmission of primary users (PUs) as relays. Our aim is to determine the cooperation strategies among the primary and secondary systems so as to maximize the sum-rate of SUs while maintaining quality-of-service (QoS) requirements of PUs. We formulate a joint optimization problem of PU transmission mode selection, SU (or relay) selection, subcarrier assignment, power control, and time allocation. By applying dual method, this mixed integer programming problem is decomposed into parallel per-subcarrier subproblems, with each determining the cooperation strategy between one PU and one SU. We show that, on each leased subcarrier, the optimal strategy is to let a SU exclusively act as a relay or transmit for itself. This result is fundamentally different from the conventional spectrum leasing in single-channel systems where a SU must transmit a fraction of time for itself if it helps the PU's transmission. We then propose a subgradient-based algorithm to find the asymptotically optimal solution to the primal problem in polynomial time. Simulation results demonstrate that the proposed algorithm can significantly enhance the network performance.

• Advances in concrete construction
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• 제11권1호
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.1-15
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• 2007

Consider a Rayleigh distribution with $$pdf\;p(x/{\theta})\;=\;2x{\theta}^{-1}\;{\exp}\;({-x^2}/{\theta})$$ and mean lifetime ${\mu}\;=\;\sqrt{\pi\theta}/2$. We study the two-action problem of testing the hypotheses $H_{0}\;:\;{\mu}{\leq}{\mu}_{0}$ against $H_{1}\;:\;{\mu}\;>\;{\mu}_{0}$ using a linear error loss of ${\mid}{\mu}\;-\;{\mu}_{0}{\mid}$ via the empirical Bayes approach. We construct a monotone empirical Bayes test ${\delta}^{*}_{n}$ and study its associated asymptotic optimality. It is shown that the regret of ${\delta}^{*}_{n}$ converges to zero at a rate $\frac{{\ln}^{2}n}{n}$, where n is the number of past data available when the present testing problem is considered.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1979년도 하계 전자.전기연합학술발표회논문집
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• pp.80-82
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• 1979

While the standard linear-quadratic-Gaussian problem has fixed horizons, this paper considers the LQG problem with moving horizons. By the separation principle the solution will be given by the kalman filter with the approaching horizon and the LQ regulator with the receding horizon. Sufficient conditions on weighting matrices are derived under which the filter and regulator are asymptotically stable. It wall be shown that the computation method of the moving-horizon LQG regulators is better than that of the standard LQG regulator. The performance measure between the two optimal controls will be compared. A simulation result is given in order to show the usefulness of the moving-horizon LQG regulator.s

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3394-3408
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• 2014

Conventional mobile state (MS) and base station (BS) association based on average signal strength often results in imbalance of cell load which may require more powerful processor at BSs and degrades the perceived transmission rate of MSs. To deal with this problem, a Markov decision process (MDP) for load balancing in a multi-cell system with multi-carriers is formulated. To solve the problem, exploiting Sarsa algorithm of on-line learning type [12], ${\alpha}$-controllable load balancing algorithm is proposed. It is designed to control tradeoff between the cell load deviation of BSs and the perceived transmission rates of MSs. We also propose an ${\varepsilon}$-differential soft greedy policy for on-line learning which is proven to be asymptotically convergent to the optimal greedy policy under some condition. Simulation results verify that the ${\alpha}$-controllable load balancing algorithm controls the behavior of the algorithm depending on the choice of ${\alpha}$. It is shown to be very efficient in balancing cell loads of BSs with low ${\alpha}$.

• Journal of Multimedia Information System
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• 제2권1호
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• pp.143-152
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• 2015

Multimedia services in the cloud have become a popular trend in the big data environment. However, how to efficiently schedule a large number of multimedia services in the cloud is still an open and challengeable problem. Current cloud-based scheduling algorithms exist the following problems: 1) the content of the multimedia is ignored, and 2) the cloud platform is a known parameter, which makes current solutions are difficult to utilize practically. To resolve the above issues completely, in this work, we propose a novel distributed multimedia scheduling to satisfy the objectives: 1) Develop a general cloud-based multimedia scheduling model which is able to apply to different multimedia applications and service platforms; 2) Design a distributed scheduling algorithm in which each user makes a decision based on its local information without knowing the others' information; 3) The computational complexity of the proposed scheduling algorithm is low and it is asymptotically optimal in any case. Numerous simulations have demonstrated that the proposed scheduling can work well in all the cloud service environments.