A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absol...A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.展开更多
The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm,...The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square(LS) method and the maximum likelihood estimation(MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.展开更多
Soft output Viterbi algorithm （SOVA） is a turbo decoding algorithm that is suitable for hardware implementation. But its performance is not so good as maximum a posterior probability（MAP） algorithm. So it is very ...Soft output Viterbi algorithm （SOVA） is a turbo decoding algorithm that is suitable for hardware implementation. But its performance is not so good as maximum a posterior probability（MAP） algorithm. So it is very important to improve its performance. The non-correlation between minimum and maximum likelihood paths in SOVA is analyzed. The metric difference of both likelihood paths is used as iterative soft information, which is not the same as the traditional SOVA. The performance of the proposed SOVA is demonstrated by the simulations. For 1 024-bit frame size and 9 iterations with signal to noise ratio from 1 dB to 4 dB, the experimental results show that the new SOVA algorithm obtains about more 0. 4 dB and 0. 2 dB coding gains more than the traditional SOVA and Bi-SOVA algorithms at bit error rate（BER） of 1 × 10^-4 , while the latency is only half of the Bi-direction SOVA decoding.展开更多
基金The US National Science Foundation (No. CMMI-0408390,CMMI-0644552,BCS-0527508)the National Natural Science Foundation of China (No. 51010044,U1134206)+2 种基金the Fok YingTong Education Foundation (No. 114024)the Natural Science Foundation of Jiangsu Province (No. BK2009015)the Postdoctoral Science Foundation of Jiangsu Province (No. 0901005C)
文摘A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.
基金Project(51165019)supported by the National Natural Science Foundation of ChinaProject(1308RJYA018)supported by Gansu Provincial Natural Science Fund,ChinaProject(2013-4-110)supported by Lanzhou Technology Development Program,China
文摘The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square(LS) method and the maximum likelihood estimation(MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.
基金Guangzhou Science and Technology Project(2004Z3 -D0321) Guangdong Science and Technology Project(200510101013)
文摘Soft output Viterbi algorithm （SOVA） is a turbo decoding algorithm that is suitable for hardware implementation. But its performance is not so good as maximum a posterior probability（MAP） algorithm. So it is very important to improve its performance. The non-correlation between minimum and maximum likelihood paths in SOVA is analyzed. The metric difference of both likelihood paths is used as iterative soft information, which is not the same as the traditional SOVA. The performance of the proposed SOVA is demonstrated by the simulations. For 1 024-bit frame size and 9 iterations with signal to noise ratio from 1 dB to 4 dB, the experimental results show that the new SOVA algorithm obtains about more 0. 4 dB and 0. 2 dB coding gains more than the traditional SOVA and Bi-SOVA algorithms at bit error rate（BER） of 1 × 10^-4 , while the latency is only half of the Bi-direction SOVA decoding.
