Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

In-situ crystal growth and photoluminescence properties of YBO3: Tb3+ microstructures

Three-dimensional (3D) YBO₃:Tb³⁺ flower-like and dense flower-like hierarchitecture constituted of nanoflakes are solvothermally synthesized in the presence of polyborate precursors in the mixture of ethanol and water. The growth process of the YBO₃:Tb³⁺ flowers and dense flowers was explored based on the time-dependent experiment and the results showed that the growth mechanism follows an in situ growth rather than self-assembly process as reported previously. YBO₃:Tb³⁺ morphologies composed of nanoflakes are achieved by controlling the concentration of ethanol and dependence of photoluminescence on morphology was studied. Remarkable photoluminescence enhancement was observed for YBO₃:Tb³⁺ with flower-like morphology demonstrating the potential of the microstructure in future applications as a green phosphor. Such a synthetic method and growth mechanism may be applied to fabricate complex 3D architectures of other materials.     

基于Q-learning的不确定环境BDI Agent最优策略规划研究

BDI模型能够很好地解决在特定环境下的Agent的推理和决策问题,但在动态和不确定环境下缺少决策和学习的能力。强化学习解决了Agent在未知环境下的决策问题,却缺少BDI模型中的规则描述和逻辑推理。针对BDI在未知和动态环境下的策略规划问题,提出基于强化学习Q-learning算法来实现BDI Agent学习和规划的方法,并针对BDI的实现模型ASL的决策机制做出了改进,最后在ASL的仿真平台Jason上建立了迷宫的仿真,仿真实验表明,在加入Q-learning学习机制后的新的ASL系统中,Agent在不确定环境下依然可以完成任务。     

土壤中石油污染物的脱附过程

文章中主要采用有机溶剂萃取法（OSE）对高浓度石油污染土壤进行修复，经过精馏操作，有效回收原油，建立脱附等温曲线，研究了石油污染物在土壤-有机溶剂两相间的迁移规律。     

变频器调速装置节能浅析

1概述 近十多年来,随着电力电子技术、微电子技术及现代控制理论的发展,变频调速技术已经广泛地用于交流电动机的速度控制,其最主要的特点是具有高效率的驱动性能及良好的控制特性.它是一种高新技术电力节能装置,它通过改变（降低）电源工作频率,来降低动力设备（电机）的转速,减少设备的输出功率,达到输出功率与工作负荷的最佳匹配,实现节能目的,有效地提高了经济效益和产品质量.几乎可以说,有电动机的地方就有变频器,在一切需要进行速度控制的场合,变频器以其操作方便、体积小、控制性能高而获得广泛应用.     

动态规划原理在骨料用料方案设计中的应用

在砂石料场规划中常面临多供料点、多需料点条件下的用料方案设计问题。本文应用动态规划原理,提出了“多供点—多需点”物料调配优化计算方法及框图,编制了相应的计算程序。工程实例分析表明,该方法在骨料综合成本和弃料量等方面均优于常规方法,具有一定的应用价值。     

乌江索风营水电站地下厂房岩锚梁镜面混凝土施工技术

镜面混凝土是水电工程建设中新兴的一门施工技术,在乌江索风营水电站地下厂房岩锚梁混凝土施工中采用了镜面混凝土,这在技术上是一次大胆的尝试和创新,既满足了功能上的要求,同时也达到了美观和装饰地下厂房的效果,为今后水电工程施工中广泛采用镜面混凝土技术提供了样本和宝贵经验。