[Background]High harmonic cavities are widely used in electron storage rings to lengthen thebunch,lower the bunch peak current,thereby reducing the IBS effect,enhancing the Touschek lifetime,as well asproviding Landau...[Background]High harmonic cavities are widely used in electron storage rings to lengthen thebunch,lower the bunch peak current,thereby reducing the IBS effect,enhancing the Touschek lifetime,as well asproviding Landau damping,which is particularly important for storage rings operating with ultra-low emittance or atlow beam energy.[Purpose]To further increase the bunch length without additional hardware costs,the phasemodulation in a dual-RF system is considered.[Methods]In this paper,turn-by-turn simulations incorporating randomsynchrotron radiation excitation are conducted,and a brief analysis is presented to explain the bunch lengtheningmechanism.[Results]Simulation results reveal that the peak current can be further reduced,thereby mitigating IBSeffects and enhancing the Touschek lifetime.Although the energy spread increases,which tends to reduce thebrightness of higher-harmonic radiation from the undulator,the brightness of the fundamental harmonic can,in fact,beimproved.展开更多
While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance re...While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.展开更多
针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现...针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现设备状态实时映射,结合LS-SVM建立多变量动态预测模型,并引入多目标微分进化算法(MODE)进行参数优化。实际应用表明,该方法使主汽温波动范围从±7℃缩小至±2.5℃,再热汽温预测误差稳定在±1.5℃以内,年节约燃煤成本超400万元,为火电机组深度调峰与能效提升提供技术支撑。展开更多
针对煤与瓦斯突出事故的复杂性以及数据获取困难导致预测准确率低的问题,提出基于密度的噪声应用空间聚类-改进哈里斯鹰优化-支持向量机(density based spatial clustering of applications with noise-improved Harris hawks optimizat...针对煤与瓦斯突出事故的复杂性以及数据获取困难导致预测准确率低的问题,提出基于密度的噪声应用空间聚类-改进哈里斯鹰优化-支持向量机(density based spatial clustering of applications with noise-improved Harris hawks optimization-support vector machine, DBSCAN-IHHO-SVM)预测模型。首先,选取瓦斯含量、瓦斯压力、煤层孔隙率、煤层坚固性系数作为预测指标,对数据中的缺失值采用均值填补处理,利用生成式对抗网络(generative adversarial network, GAN)扩充突出数据量。接着,采用DBSCAN从非突出数据中识别潜在危险数据,并将其作为新的突出数据。最后,引入IHHO调整SVM模型参数,将处理后的数据输入IHHO-SVM模型进行预测分析。结果表明,相比于原始SVM模型,DBSCAN-IHHO-SVM模型的整体预测准确率、危险数据识别率分别提升了5.87%、38.46%。在突出数据样本有限的情况下,DBSCAN-IHHO-SVM模型能有效挖掘非突出数据潜在信息,实现精准预警,为该领域研究提供了新思路。展开更多
基金National Natural Science Foundation of China(12405168)The Fundamental Research Funds for the Central Universities,China(2024CDJXY004)。
文摘[Background]High harmonic cavities are widely used in electron storage rings to lengthen thebunch,lower the bunch peak current,thereby reducing the IBS effect,enhancing the Touschek lifetime,as well asproviding Landau damping,which is particularly important for storage rings operating with ultra-low emittance or atlow beam energy.[Purpose]To further increase the bunch length without additional hardware costs,the phasemodulation in a dual-RF system is considered.[Methods]In this paper,turn-by-turn simulations incorporating randomsynchrotron radiation excitation are conducted,and a brief analysis is presented to explain the bunch lengtheningmechanism.[Results]Simulation results reveal that the peak current can be further reduced,thereby mitigating IBSeffects and enhancing the Touschek lifetime.Although the energy spread increases,which tends to reduce thebrightness of higher-harmonic radiation from the undulator,the brightness of the fundamental harmonic can,in fact,beimproved.
基金funding from the National Key Research and Development Program of China(No.2018YFE0110000)the National Natural Science Foundation of China(No.11274259,No.11574258)the Science and Technology Commission Foundation of Shanghai(21DZ1205500)in support of the present research.
文摘While reinforcement learning-based underwater acoustic adaptive modulation shows promise for enabling environment-adaptive communication as supported by extensive simulation-based research,its practical performance remains underexplored in field investigations.To evaluate the practical applicability of this emerging technique in adverse shallow sea channels,a field experiment was conducted using three communication modes:orthogonal frequency division multiplexing(OFDM),M-ary frequency-shift keying(MFSK),and direct sequence spread spectrum(DSSS)for reinforcement learning-driven adaptive modulation.Specifically,a Q-learning method is used to select the optimal modulation mode according to the channel quality quantified by signal-to-noise ratio,multipath spread length,and Doppler frequency offset.Experimental results demonstrate that the reinforcement learning-based adaptive modulation scheme outperformed fixed threshold detection in terms of total throughput and average bit error rate,surpassing conventional adaptive modulation strategies.
文摘针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现设备状态实时映射,结合LS-SVM建立多变量动态预测模型,并引入多目标微分进化算法(MODE)进行参数优化。实际应用表明,该方法使主汽温波动范围从±7℃缩小至±2.5℃,再热汽温预测误差稳定在±1.5℃以内,年节约燃煤成本超400万元,为火电机组深度调峰与能效提升提供技术支撑。
