核动力装置冷凝器体积的优化设计

冷凝器是核动力装置中的重要设备,它的尺寸是影响核动力装置经济性和合理布置的重要因素。本文在总结冷凝器设计经验的基础上,建立了冷凝器的数学模型,并开发了相应的计算程序。在此基础上对影响冷凝器体积的设计参数进行了敏感性分析。以冷凝器总体积最小为目标函数,在所确定的约束条件下,采用复合形 遗传算法对冷凝器进行了优化设计。结果表明,参考冷凝器的设计并非最优方案,尚有较大的优化空间。也证明了该优化设计方法的可行性。本文的计算结果可为工程设计提供参考。     

Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network

 Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel. Experiments were carried out to characterize the hardening qualities. A predicting and optimizing model using genetic algorithm-back propagation neural network (GA-BP) was developed based on the experimental results. The non-linear relationship between properties of hardening layers and process parameters was established. The results show that the GA-BP predicting model is reliable since prediction results are in rather good agreement with measured results. The optimal properties of the hardened layer were deduced from GA. And through multi optimizations, the optimum comprehensive performances of the hardened layer were as follows: plasma arc current is 90 A, hardening speed is 22 m/min, plasma gas flow rate is 60 L/min and hardening distance is 43 mm. It concludes that GA-BP mode developed in this study provides a promising method for plasma hardening parameters prediction and optimization.     

锥束CT扫描方式对ART算法图像重建的影响研究

平板探测器锥束CT的扫描方式对ART(Algebraic Reconstruction Techniques)算法图像重建会产生影响.本文用128×128×128三维Shepp-Logan头部模型作为仿真对象,探讨了不同源-扫描中心距离、投影数、缺角度下扫描方式对图像重建的影响.结果表明,源-扫描中心距离与扫描中心-探...     

A box constrained gradient projection algorithm for compressed sensing

A new algorithm is presented which aims to solve problems from compressed sensing - under-determined problems where the solution vector is known a priori to be sparse. Upper bounds on the solution vector are found so that the problem can be reformulated as a box-constrained quadratic programme. A sparse solution is sought using a Barzilai-Borwein type projection algorithm. New insight into the choice of step length is provided through a study of the special structure of the underlying problem together with upper bounds on the step length. Numerical experiments are conducted and results given, comparing this algorithm with a number of other current algorithms.     

Adaptive parallel interference cancellation for CDMA systems—A weight selection and filtering scheme

Parallel interference cancellation (PIC) is a well-known multiuser detection algorithm in direct-sequence code-division multiple-access (DS-CDMA) systems. It is typically implemented with a multi-stage architecture. One problem associated with the PIC is that unreliable interference cancellation may occur in the early stages and the system performance may be degraded. Thus, the partial PIC detector was developed to control the cancellation level by use of interference cancellation factors. Partial PIC can be implemented with an adaptive form, in which optimal weights are derived using the least mean square (LMS) algorithm. In this paper, we propose an algorithm improving the conventional adaptive partial PIC. The main idea is to reduce the number of active weights in the LMS algorithm, and to perform weight post-filtering such that the resultant excess mean square error can be reduced. We also analyze the performance of the proposed algorithm and derive the bit error rate of the second stage output. Simulation results verify that the proposed algorithm outperforms the conventional partial PIC, and derived analytical results are accurate.     

A two-stage affine projection algorithm with mean-square-error-matching step-sizes

This paper proposes a two-stage affine projection algorithm (APA) with different projection orders and step-sizes. The proposed algorithm has a high projection order and a fixed step-size to achieve fast convergence rate at the first stage and a low projection order and a variable step-size to achieve small steady-state estimation errors at the second stage. The stage transition moment from the first to the second stage is determined by examining, from a stochastic point of view, whether the current error reaches the steady-state value. Moreover, in order to prevent the sudden drop of convergence rate on switching from a high projection order to a low projection order, a matching step-size method has been introduced to determine the initial step-size of the second stage by matching the mean-square errors (MSEs) before and after the transition moment. In order to continuously reduce steady-state estimation errors, the proposed algorithm adjusts the step-size of the second stage by employing a simple algorithm. Because of the reduced projection orders and variable step-size in the steady-state, the algorithm achieves improved performance as well as extremely low computational complexity as compared to the existing APAs with selective input vectors and APAs with variable step-size.     

A noise constrained least mean fourth (NCLMF) adaptive algorithm

The learning speed of an adaptive algorithm can be improved by properly constraining the cost function of the adaptive algorithm. In this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is proposed. The NCLMF algorithm is obtained by constraining the cost function of the standard LMF algorithm to the fourth-order moment of the additive noise. The NCLMF algorithm can be seen as a variable step-size LMF algorithm. The main aim of this work is to derive the NCLMF adaptive algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm.     

Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry

In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS) based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio and computational complexity.     

Anisotropic diffusion for noise removal of band pass signals

A noise removal method for band pass signals based on the anisotropic diffusion algorithm originally put forward by Perona and Malik is proposed in this paper. The anisotropic smoothing algorithm proposed here is for band pass signals modulated with a constant carrier frequency. A partial differential equation to smooth band pass noisy signals is derived. The propagator of this differential equation is also analytically calculated in this paper. An appropriate linear operator is then considered here for such band pass signals to form an anisotropic diffusion algorithm. The algorithm proposed here demonstrates better performance for band pass noisy signals containing discontinuities in comparison with the traditional Perona-Malik (PM) algorithm and is robust in the presence of excessive noise with SNR less than unity.     

Low power finite state machine synthesis using power-gating

Power-gating turns off the power supply of a portion of the circuit completely, resulting in total elimination of power consumption for that part. However, it also necessitates that the sub-circuit to be activated should be charged for some time before its activation. This critical issue can influence the decomposition of a finite state machine (FSM) for its power gated implementation. In this paper we have presented a power-gating method that integrates FSM partitioning with state encoding, thus providing a total solution to the problem of power-aware FSM synthesis. It shows better results, in terms of dynamic and leakage power consumption, compared to the existing techniques reported in the literature.