水轮机调节系统的状态PI控制器研究

虽然状态PI控制器已经提出很多年,但至今未在水轮机转速控制中有所应用。本文应用状态PI控制,从水轮机控制系统状态反馈模型出发,设计水轮机调节系统状态PI控制器,并对状态PI最优控制与传统推荐PID最佳调节进行比较分析。仿真结果表明:两者动态过程明显不同,前者的动态过程呈现非周期快速稳定特点,而后者的动态过程则为衰减振荡过程,并且状态PI控制在负荷变化时有更好的控制品质。     

机器人足球比赛系统中的机器人任务分配方法

针对基于遗传算法的多机器人任务分配方法中,由于初始种群是随机产生不能很好地表征整个解空间,而容易陷入局部最优解的问题,提出一种新的多机器人任务分配方法。该方法根据机器人效用函数值来确定个体基因,从而产生初始种群,并引进分层遗传算法来实现具有不同最优基因的群体分开演化。仿真实验表明,该方法比传统的遗传算法有更高的寻优效率和更快的收敛速度。     

动态规划法求解加工顺序问题的研究

提出动态规划法求解加工顺序问题的方法,分析最优子结构性质,建立最优值的递归关系式,分析Johnson-Bell-man’s Rule并设计算法流程,最后通过构造实例表明了算法的有效性。     

基于神经网络的电子地图最优路径研究

现阶段电子地图的应用已经深入到各行各业,最优路径的查找及规划作为GIS的一个关键问题,成为研究的一个难点,本文提出一种基于Hopfield神经网络的方法来解决地图最优路径问题。以物流配送中的车辆路径最优化为例,先根据Hopfield神经网络模型研究基于Hopfield的最优路径算法,然后探讨和验证该算法的可行性、实用性,最后通过算例分析对该方法进行验证。     

考虑蓄水期弃水风险的水库长期发电调度方法

短期径流的大幅波动使得以平均径流为基础制定的水库长期调度方案面临较大弃水风险,是影响水库长期调度决策合理性的重要因素。本文考虑日尺度径流波动影响,提出一种蓄水期弃水风险量化方法,并建立了耦合弃水风险的水库长期发电调度模型。利用长系列日径流资料,结合风险最小蓄水规则,以不蓄弃水流量为指标量化蓄水期各月弃水风险;采用Copula函数构建月均入流与弃水风险的联合分布和条件概率分布,明晰了特定入流条件的风险置信区间;最后,将弃水风险以弃电损失函数融入优化模型,以获得更符合实际的长期调度方案。以澜沧江流域小湾水库为工程背景进行调度模拟分析,结果表明本文方法能够有效降低水库长期优化调度方案的弃水风险,有利于提高优化结果的可操作性,与传统方法相比,能够使多年平均弃水减少约4.76亿m³、发电量增加约1.15亿kW·h。     

金沙江中游梯级水电站联合优化调度实践

分析了金沙江中游区域梯级水电站联合调度的特殊性及复杂性,从电调的角度介绍了金中集控中心自成立以来对受控梯级水电站联合调度的具体实施,总结了梯级水电站联合优化调度的措施,并分析了梯级水电站联合优化调度的综合效益,发现优化调度成效明显。     

基于时序特征的黄河三角洲土地覆盖动态及驱动力分析

综合不同土地利用类型的Landsat影像时序特征和面向对象分割方法,分析1985-2018年现代黄河三角洲土地覆盖时空特征,探讨土地覆盖变化的驱动机制。结果表明:1985-2018年间研究区天然湿地面积大幅减少,共减少9.9847×104 hm²,其中沼泽和草甸湿地面积减少尤为明显,而人工湿地面积则逐年增加,年平均增加率达54.66%,主要是盐田养殖场和水稻种植面积的增大;社会经济因素对研究区土地覆盖的影响程度高于水文因素;输沙量是影响研究区土地覆盖变化的主要水文因素,实测径流量的减少进一步导致天然湿地向旱地类型转化;人口和GDP变化对人工湿地面积增加的正相关作用比较显著,而天然湿地面积的减少则与该两个指标的负相关关系比较明显。     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.     

Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation

Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.     

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.