In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In...In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In addition, in order to neglect the effect of sound reflection from boundaries, necessary treatment is conducted, which makes the method more efficient. Moreover, this method is combined with the sound propagation algorithms to predict the sound radiated from a cylindrical shell in waveguide. Numerical simulations show the effect of how reflections can be neglected if the distance between the structure and the boundary exceeds the maximum linear dimension of the structure. It also shows that the reflection from the bottom of the waveguide can be approximated by plane wave conditionally. The proposed method is more robust and efficient in computation, which can be used to predict the acoustic radiation in waveguide.展开更多
This paper studied a fast recursive predictive algorithm used for medical X-ray image compression. This algorithm consists of mathematics model building, fast recursive algorithm deducing, initial value determining, s...This paper studied a fast recursive predictive algorithm used for medical X-ray image compression. This algorithm consists of mathematics model building, fast recursive algorithm deducing, initial value determining, step-size selecting, image compression encoding and original image recovering. The experiment result indicates that this algorithm has not only a higher compression ratio to medical X-ray images compression, but also promotes image compression speed greatly.展开更多
The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shif...The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shift is used in the simulation. The channel estimator obtains the sampled channel impulse response (SIR) from the predetermined training sequence. The FTF is a computationally efficient implementation of the recursive least squares (RLS) algorithm of the conventional Kalman filter. A stabilization FTF is used to overcome the problem caused by the accumulation of roundoff errors, and, in addition, degree-one prediction is incorporated into the algorithm (Predictive FTF) to improve the estimation performance and to track changes of the mobile channel. The efficiency of the algorithm is confirmed by simulation results for slow and fast varying mobile channel. The results show about 5 to 15 dB improvement in the Mean Square Error (Deviation) between the estimated taps and the actual ones depending on the speed of channel time variations. Slow and fast vehicular channels with Doppler frequencies 100 Hz and 222 Hz respectively are used in these tests. The predictive FTF (PFTF) algorithm give a better channel SIR estimation performance than the conventional FTF algorithm, and it involves only a small increase in complexity.展开更多
Scheduling chain combination is the core of chain-based scheduling algorithms, the speed of which determines the overall performance of corresponding scheduling algorithm. However, backtracking is used in general comb...Scheduling chain combination is the core of chain-based scheduling algorithms, the speed of which determines the overall performance of corresponding scheduling algorithm. However, backtracking is used in general combination algorithms to traverse the whole search space which may introduce redundant operations, so performance of the combination algorithm is generally poor. A fast scheduling chain combination algorithm which avoids redundant operations by skipping “incompatible” steps of scheduling chains and using a stack to remember the scheduling state is presented in this paper to overcome the problem. Experimental results showed that it can improve the performance of scheduling algorithms by up to 15 times. By further omitting unnecessary operations, a fast algorithm of minimum combination length prediction is developed, which can improve the speed by up to 10 times.展开更多
直流备用电源是变电站安全稳定运行的重要保证,厂站中目前常用的铅酸蓄电池存在着寿命低、温度性能差的问题。锂离子电池的长循环寿命、高能量密度等特点,近年随着技术不断成熟,有望成为替代方案。电池健康状态(state of health,SOH)是...直流备用电源是变电站安全稳定运行的重要保证,厂站中目前常用的铅酸蓄电池存在着寿命低、温度性能差的问题。锂离子电池的长循环寿命、高能量密度等特点,近年随着技术不断成熟,有望成为替代方案。电池健康状态(state of health,SOH)是锂离子电池储能系统可靠运行所需的核心参数,而电化学阻抗谱(electrochemical impedance spectroscopy,EIS)作为一种无损检测的方法,可用来评估电池的SOH并分析其老化的主要机制。针对静态EIS在电池工作情况下获取困难、带直流偏置的快速EIS可解释性不足的问题,本研究提出了一种基于快速阻抗谱可解释性增强的锂离子电池健康状态估计方法,在基本不影响直流电源工作的情况下快速完成电池老化预测与老化机制分析。首先,利用卷积-长短期记忆网络模型实现了动态到静态的EIS预测,卷积网络提取关键特征,长短期记忆神经网络捕捉序列间依赖关系,以实现电池老化机理解析;其次,提出了一种基于极限梯度提升算法及EIS的电池SOH估计方法,捕捉静态EIS与SOH之间的高度非线性映射关系,完成了电池SOH的在线评估,并依靠特征分裂增益量化不同频域特征的贡献以分析EIS的不同形式在预测结果中的重要性。实验表明,所提静态EIS预测方法的平均绝对误差(mean absolute error,MAE)为1.75×10-5;电池SOH估计结果的MAE仅为2.43%,电解液损失是所用电池老化的主要原因。展开更多
以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预...以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预测精度。通过比较各优化算法的模型性能,为土壤水分预测提供更优的建模方法。结果表明,TVFEMD分解效果主要受带宽阈值和B样条阶数2个关键参数影响。采用IVYA算法优化这2个参数可提升时间序列分解质量,进而改善模型预测性能。TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在训练集上表现出卓越的预测性能,其平均绝对百分比误差(MAPE)为0.002%~0.077%,决定系数(R^(2))为0.9997~1.0000;预测集中的MAPE为0.006%~0.459%,R^(2)为0.9966~1.0000。与TVFEMD-PSO-FLN模型相比,TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在拟合性能和预测精度方面均有明显提升。采用BLSO、AO、IVYA和EGO算法优化FLN超参数可有效提升模型性能,其中IVYA算法的优化效果较突出。展开更多
基金Foundation item： Supported by the National Natural Science Foundation of China under Grant No. 11274080, and the Young Scientists Fund of the National Natural Science Foundation of China under Grant No. 11404313.
文摘In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In addition, in order to neglect the effect of sound reflection from boundaries, necessary treatment is conducted, which makes the method more efficient. Moreover, this method is combined with the sound propagation algorithms to predict the sound radiated from a cylindrical shell in waveguide. Numerical simulations show the effect of how reflections can be neglected if the distance between the structure and the boundary exceeds the maximum linear dimension of the structure. It also shows that the reflection from the bottom of the waveguide can be approximated by plane wave conditionally. The proposed method is more robust and efficient in computation, which can be used to predict the acoustic radiation in waveguide.
文摘This paper studied a fast recursive predictive algorithm used for medical X-ray image compression. This algorithm consists of mathematics model building, fast recursive algorithm deducing, initial value determining, step-size selecting, image compression encoding and original image recovering. The experiment result indicates that this algorithm has not only a higher compression ratio to medical X-ray images compression, but also promotes image compression speed greatly.
文摘The aim of this research paper is to improve the performance of Fast Transversal Filter (FTF) adaptive algorithm used for mobile channel estimation. A multi-ray Jakes mobile channel model with a Doppler frequency shift is used in the simulation. The channel estimator obtains the sampled channel impulse response (SIR) from the predetermined training sequence. The FTF is a computationally efficient implementation of the recursive least squares (RLS) algorithm of the conventional Kalman filter. A stabilization FTF is used to overcome the problem caused by the accumulation of roundoff errors, and, in addition, degree-one prediction is incorporated into the algorithm (Predictive FTF) to improve the estimation performance and to track changes of the mobile channel. The efficiency of the algorithm is confirmed by simulation results for slow and fast varying mobile channel. The results show about 5 to 15 dB improvement in the Mean Square Error (Deviation) between the estimated taps and the actual ones depending on the speed of channel time variations. Slow and fast vehicular channels with Doppler frequencies 100 Hz and 222 Hz respectively are used in these tests. The predictive FTF (PFTF) algorithm give a better channel SIR estimation performance than the conventional FTF algorithm, and it involves only a small increase in complexity.
基金Project (No. Y105355) supported by the Natural Science Foundationof Zhejiang Province, China
文摘Scheduling chain combination is the core of chain-based scheduling algorithms, the speed of which determines the overall performance of corresponding scheduling algorithm. However, backtracking is used in general combination algorithms to traverse the whole search space which may introduce redundant operations, so performance of the combination algorithm is generally poor. A fast scheduling chain combination algorithm which avoids redundant operations by skipping “incompatible” steps of scheduling chains and using a stack to remember the scheduling state is presented in this paper to overcome the problem. Experimental results showed that it can improve the performance of scheduling algorithms by up to 15 times. By further omitting unnecessary operations, a fast algorithm of minimum combination length prediction is developed, which can improve the speed by up to 10 times.
文摘以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预测精度。通过比较各优化算法的模型性能,为土壤水分预测提供更优的建模方法。结果表明,TVFEMD分解效果主要受带宽阈值和B样条阶数2个关键参数影响。采用IVYA算法优化这2个参数可提升时间序列分解质量,进而改善模型预测性能。TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在训练集上表现出卓越的预测性能,其平均绝对百分比误差(MAPE)为0.002%~0.077%,决定系数(R^(2))为0.9997~1.0000;预测集中的MAPE为0.006%~0.459%,R^(2)为0.9966~1.0000。与TVFEMD-PSO-FLN模型相比,TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在拟合性能和预测精度方面均有明显提升。采用BLSO、AO、IVYA和EGO算法优化FLN超参数可有效提升模型性能,其中IVYA算法的优化效果较突出。
