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| 1 | 基于二维卡尔曼滤波与最小二乘法的目标定位算法显示文摘为了解决当前圆目标检测算法在噪声干扰与目标特征微弱的情况下,使其无法精确检测自动检测工件上的圆目标问题,论文提出了基于二维卡尔曼滤波与最小二乘法的工件圆目标定位检测算法。首先,基于一维卡尔曼滤波,利用图像像素灰度特性代替其一维信号模型,推导用于二维图像的卡尔曼滤波机制,有效滤除图像噪声,提取圆目标轮廓。引入最小二乘法,建立回归圆方程,拟合出目标圆轮廓,计算出圆心位置与半径,完成圆目标检测。实验测试结果显示:与当前工件图像圆目标检测算法相比,在面对较多噪声干扰而目标特征微弱时,论文算法拥有更高定位检测准确度与鲁棒性,能够较好地抵御噪声的影响。 | 马羽 高蕾 | 2017 | 计算机与数字工程2017，45，8： | 5 |
| 2 | A Novel Hybrid FA-Based LSSVR Learning Paradigm for Hydropower Consumption Forecasting显示文摘Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm(FA) into least square support vector regression(LSSVR), i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence(AI) technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is especially introduced to address the crucial but difficult task of parameters determination in LSSVR(e.g.,hyper and kernel function parameters). With the Chinese hydropower consumption as sample data,the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models(including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools), in terms of level and directional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data. | TANG Ling WANG Zishu LI Xinxie YU Lean ZHANG Guoxing | 2015 | Journal of Systems Engineering and Electronics2015，26，5： | 3 |
| 3 | 基于选择性深度集成的集装箱吞吐量混合预测模型研究显示文摘精准预测集装箱吞吐量是合理规划港口建设、制定港口作业计划和调整港口发展方向的重要基础.针对港口集装箱吞吐量的复杂非线性特征,本文提出了基于选择性深度集成的集装箱吞吐量混合预测模型(HMSD).该模型首先使用经验模态分解方法将原始集装箱吞吐量时间序列分为若干个本征模函数和余波序列.考虑到各本征模函数的高度非线性特征,分别训练长短时记忆网络、门控循环单元和卷积神经网络三种深度学习模型作为基准模型对其进行预测,再运用改进的数据分组处理技术(GMDH)建立选择性深度集成模型,得到各本征模函数的集成预测结果,进一步采用自回归求积移动平均模型预测线性余波序列,将全部本征模函数和余波的预测值进行整合得到总的集装箱吞吐量预测值.为验证提出的模型在集装箱吞吐量预测上的性能,本文选取我国吞吐量具有显著差异的六个港口进行实证,结果表明该模型在六个港口上均具有最好的预测效果,最后还运用HMSD模型给出了2021年1月至2022年12月六个港口集装箱吞吐量的样本外预测值. | 肖进 文章 刘博 陈茗洋 王亚东 黄静 | 2022 | 系统工程理论与实践2022，42，4： | 2 |
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