Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis of the disease is key to its effective treatment and management. Identification of plasmodium parasites life stages and species forms part of...Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis of the disease is key to its effective treatment and management. Identification of plasmodium parasites life stages and species forms part of the diagnosis. In this study, a technique for identifying the parasites life stages and species using microscopic images of thin blood smears stained with Giemsa was developed. The technique entailed designing and training Artificial Neural Network (ANN) classifiers to perform the classification of infected erythrocytes into their respective stages and species. The outputs of the system were compared to the results of expert microscopists. A total of 205 infected erythrocytes images were used to train and test the performance of the system. The system recorded 99.9% in recognizing stages and 96.2% in recognizing plasmodium species.展开更多
省域铁路成网条件下列车开行方案涉及线路制式、等级以及列车种类等多因素影响,叠加客流选择的多样性,使优化问题更加复杂化。为刻画网络条件下客流和列车流的耦合,利用深度优先搜索(Depth First Search,DFS)算法构建客流径路备选集;基...省域铁路成网条件下列车开行方案涉及线路制式、等级以及列车种类等多因素影响,叠加客流选择的多样性,使优化问题更加复杂化。为刻画网络条件下客流和列车流的耦合,利用深度优先搜索(Depth First Search,DFS)算法构建客流径路备选集;基于列车开行方案的编制原则建立列车径路备选集,以列车运行成本和旅客出行总时间最小为优化目标,构建双目标非线性优化模型,设计NSGA-II(Non-dominated Sorting Genetic Algorithm-II)算法进行求解;从列车运行、旅客出行和企业运营这3个维度建立多准则评价指标体系,利用熵权-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)方法比选Pareto前沿面上的典型解,选取相对接近度最高的解作为建议方案。依托Z省铁路网进行大规模实例研究,结果表明:模型求解得到的Pareto前沿面收敛性和分布性较好,具有较强鲁棒性,与多目标粒子群优化算法(Multi-objective Particle Swarm Optimization,MOPSO)相比取得的结果更佳。通过熵权-TOPSIS方法多准则评价比选,得到方案II为该省推荐列车开行方案,省域范围内开行列车660对·d-1,相较优化前,旅客出行时间成本和列车运行成本显著降低,线路利用率过低或过高区段比例大幅减少,运能紧张区段得到有效疏解。展开更多
针对深度学习应用技术进行了研究性综述。详细阐述了RBM(受限玻尔兹曼机)逐层预训练后再用BP(反向传播)微调的深度学习贪婪层训练方法,对比分析了BP算法中三种梯度下降的方式,建议在线学习系统采用随机梯度下降,静态离线学习系统采用随...针对深度学习应用技术进行了研究性综述。详细阐述了RBM(受限玻尔兹曼机)逐层预训练后再用BP(反向传播)微调的深度学习贪婪层训练方法,对比分析了BP算法中三种梯度下降的方式,建议在线学习系统采用随机梯度下降,静态离线学习系统采用随机小批量梯度下降;归纳总结了深度学习深层结构特征,并推荐了目前最受欢迎的五层深度网络结构设计方法。分析了前馈神经网络非线性激活函数的必要性及常用的激活函数优点,并推荐Re LU(rectified linear units)激活函数。最后简要概括了深度卷积神经网络、深度递归神经网络、长短期记忆网络等新型深度网络的特点及应用场景,归纳总结了当前深度学习可能的发展方向。展开更多
文摘Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis of the disease is key to its effective treatment and management. Identification of plasmodium parasites life stages and species forms part of the diagnosis. In this study, a technique for identifying the parasites life stages and species using microscopic images of thin blood smears stained with Giemsa was developed. The technique entailed designing and training Artificial Neural Network (ANN) classifiers to perform the classification of infected erythrocytes into their respective stages and species. The outputs of the system were compared to the results of expert microscopists. A total of 205 infected erythrocytes images were used to train and test the performance of the system. The system recorded 99.9% in recognizing stages and 96.2% in recognizing plasmodium species.
文摘省域铁路成网条件下列车开行方案涉及线路制式、等级以及列车种类等多因素影响,叠加客流选择的多样性,使优化问题更加复杂化。为刻画网络条件下客流和列车流的耦合,利用深度优先搜索(Depth First Search,DFS)算法构建客流径路备选集;基于列车开行方案的编制原则建立列车径路备选集,以列车运行成本和旅客出行总时间最小为优化目标,构建双目标非线性优化模型,设计NSGA-II(Non-dominated Sorting Genetic Algorithm-II)算法进行求解;从列车运行、旅客出行和企业运营这3个维度建立多准则评价指标体系,利用熵权-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)方法比选Pareto前沿面上的典型解,选取相对接近度最高的解作为建议方案。依托Z省铁路网进行大规模实例研究,结果表明:模型求解得到的Pareto前沿面收敛性和分布性较好,具有较强鲁棒性,与多目标粒子群优化算法(Multi-objective Particle Swarm Optimization,MOPSO)相比取得的结果更佳。通过熵权-TOPSIS方法多准则评价比选,得到方案II为该省推荐列车开行方案,省域范围内开行列车660对·d-1,相较优化前,旅客出行时间成本和列车运行成本显著降低,线路利用率过低或过高区段比例大幅减少,运能紧张区段得到有效疏解。
文摘针对深度学习应用技术进行了研究性综述。详细阐述了RBM(受限玻尔兹曼机)逐层预训练后再用BP(反向传播)微调的深度学习贪婪层训练方法,对比分析了BP算法中三种梯度下降的方式,建议在线学习系统采用随机梯度下降,静态离线学习系统采用随机小批量梯度下降;归纳总结了深度学习深层结构特征,并推荐了目前最受欢迎的五层深度网络结构设计方法。分析了前馈神经网络非线性激活函数的必要性及常用的激活函数优点,并推荐Re LU(rectified linear units)激活函数。最后简要概括了深度卷积神经网络、深度递归神经网络、长短期记忆网络等新型深度网络的特点及应用场景,归纳总结了当前深度学习可能的发展方向。
