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101.
国际核试验监测系统包括地震探测系统、水声探测系统、次声探测系统、放射性核素探测系统、国际数据中心、现场核查系统等组成部分,是一个国际性的“大科学”工程。文章概要介绍了核试验监测中的主要技术问题和物理问题,国际核试验监测系统的现状和发展趋势,以及国际监测系统在科学研究和可持续发展中的可能的应用领域。 相似文献
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103.
光纤光栅传感器在大体积混凝土基础温度监测中的应用 总被引:7,自引:0,他引:7
光纤光栅传感器具有体积小、质量轻、灵敏度高、耐腐蚀、抗电磁干扰、传输频带较宽、易于进行分布式测量等诸多其他传感器所不具备的优点,更适用于现场的长期健康监测。大体积混凝土在施工过程中的温度问题如处理不当将会引起混凝土开裂。利用温度计、热电耦等作为传感器的传统的检测手段已经大大的制约了数据的准确性与精度。寻求一种高精度温度检测手段已经成为用于现场结构监测的前提。本文结合具体的工程实例介绍了光纤光栅传感器在基础混凝土温度监测中的应用,介绍了监测系统的组成,传感器的构造和标定,并利用实测温度预测基础混凝土底板中温度应力,及时采取措施防止混凝土的开裂。 相似文献
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立交桥多跨连续梁复位监测 总被引:1,自引:0,他引:1
某立交桥在长期使用中,弯桥处多跨连续梁发生横向滑移近40cm,为此对该梁进行顶升和横移的复位施工。使用位移传感器对多跨连续梁的顶升和降落进行监测和控制,同时使用应变片监测多跨连续梁的应力。监测结果表明,相邻桥墩处的连续梁最大竖向位移差在3.40~7.89mm之间,连续梁产生的最大附加应力在-2.06~1.78MPa之间,连续梁没有裂缝产生,从而确保了多跨连续梁在复位施工中的安全。同时对钢支架的应力进行监测,得出钢管混凝土柱始终处于严重偏心受压状态的结论。在长时间监测中,采取分段测试然后测试数据累加的方法,减小了应变仪连续使用中的漂移问题。该桥的监测方法也可以用于其它桥梁的施工中。 相似文献
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107.
通过集成在线富集和在线热消解技术,建立了基于微波等离子体原子发射光谱法(MP-AES)的地表水中重金属的在线检测技术,对珠江干流之一的西江水样中重金属元素(Cd,Cu,Cr,Ni,Pb,Fe,Mn和Zn)进行现场同时在线监测。结果表明,该在线检测技术对这些重金属元素的定量检测能力满足地表水环境质量标准(GB3838-2002)的限量要求;据环境标准样品中重金属元素分析结果,测定值与配制标准值一致;自来水加标样品的回收率为81.5%~102%。该检测技术对重金属的检出限为1.14~5.34μg/L,检测结果的相对标准偏差(RSD)为0.79%~9.4%,方法可满足地表水中重金属的现场、快速、连续、准确监测需求。 相似文献
108.
HUANG Hong;YANG Yichuan;WANG Long;ZHENG Fujian;WU Jian 《光子学报》2024,53(1):78-90
In clinical practice, segmentation and quantitative evaluation of target objects in pathological images provide valuable information for histopathological analysis, which is of great significance to auxiliary diagnosis and subsequent treatment. However, due to the dense distribution of cells and great morphological similarities between the cancer cells and normal cells, there are some challenges such as difficulty in feature extraction and unclear segmentation boundaries in the segmentation task of pathological images. At the same time, the traditional image segmentation methods are time-consuming and labor-intensive. They can only extract low level manual features, and the expression ability of deep discrimination features is insufficient, resulting in limited performance of traditional methods. Meanwhile, previous deep learning algorithms still suffer from two significant problems. Firstly, most networks ignore pixels that are difficult to segment, such as the boundaries of targets, which is particularly important for accurate segmentation. In addition, the problem of inconsistent semantic levels between different features are not solved, leading to low training efficiency. To address the above-mentioned problems, an end-to-end histopathological image segmentation network called Boundary Perception Network (BPNet) is proposed for improving the segmentation accuracy of histopathological images. Based on encoder-decoder structure, the encoder performs the convolutional downsampling operation to extract the feature information of the image through the Convolutional Neural Network (CNN). And the encoding process uses the feature encoder based on the EfficientNet-B4 network which is specifically used for pathological image segmentation. The decoder mainly consists of decooding blocks, Boundary Perception Module (BPM) and Adative Shuffle Channel Attention Moudule (ASCAM). In detail, the decoding block performs deconvolution operation to complete the decoding process of the feature information. Then, the BPM in the decoder stage aims to strengthen the ability of mining for difficult segmentation regions, so that the network focuses on the higher uncertainty as well as more complex edge regions, achieving feature complementarity and precision prediction results. For implementation, the BPM extracts the edge from the decoder output of each layer, and superimposes the edge information onto the encoded feature to strengthen the boundary feature information extracted from pathological images, outputting the enhanced edge perception feature map. Subsequently, the ASCAM is an improved chanel attention moudule which is used to make up the semantic gap between different levels of features, extrated by encoder, decoder and BPM, so as to further strengthens the feature understanding ability of the BPNet. This module exploits adaptive kernel size one-dimensional convolusion to capture the interactive information of local channels, at the same time ensures the efficiency and effectiveness of the training process. The obtained channel attention coefficient is multiplied by the module input feature layer to obtain the fusion feature, helping effectively learn the channel interaction information between features to improve the feature representation ability. Furthermore, a joint loss function based on structure and boundary is designed to optimize the targeting and detail processing capabilities of this method, achieving the better segmentation result of pathological images. Experiments are carried out on the Gland segmentation (GlaS) and MoNuSeg dataset, respectively. Both of the two datasets are devided into 4∶1 for training and validation. At the same time, in order to make up for the overfitting caused by the lack of training data, two kinds of online data enhancement methods of horizontal flipping and vertical flipping were carried out on the training set data in the experiment. And the four evaluation index, the Dice coefficient score, Intersection Over Union (IoU), Accuracy (ACC) and Precision (PRE), are used to evaluate the performance of this method propsed in this paper. The Dice coefficient score of the proposed method is 92.21% and 81.18%, the IoU is 85.55% and 68.34%, the ACC is 92.14% and 92.50%, the PRE is 92.07% and 75.46% on the GlaS and MoNuSeg datasets, respectively. Compared with the previous classical methods, such as U-Net, UNet++, MultiResUNet, TransUNet, UCTransNet and so on, the BPNet proposed gets the best segmentation result, especially retains more details in the segmentation boundary. Moreover, ablation experiments are carried out on the same two datasets for indicating the impacts of BPM and ASCAM. The results shows that the proposed BPM significantly optimizes the segmentation effect of the network for the edge, as well as the ASCAM makes up the semantic gap between features at different levels and further strengthens the feature understanding ability of the network. In conclusion, the BPNet proposed in this paper exploits BPM to generate edge enhancement feature maps, and uses ASCAM to seize crucial features. Finally, a joint loss function is used to capture the information of features at different levels in the output layer to achieve optimal segmentation performance. The experimental results have demonstrated that the effectiveness of each part of proposed method in the segmentation task of pathological images. 相似文献
109.
为实现白酒生产过程中出窖酒醅智能化配粮,该文开发了一套在线近红外光谱监测系统,实时在线检测出窖酒醅配粮前的水分、淀粉及酸度,实时检测结果转化为4~20mA电控输入信号,解决酒醅智能配粮问题。通过将在线近红外分析系统与台式近红外分析仪相结合,在四川宜宾六尺巷酒厂开展酒醅在线监测与配粮智能化应用。结果表明,台式仪器检测水分、淀粉、酸度的平均误差分别为-0.25%、0.38%和0.29mmol/10g,检测水分、淀粉、酸度的预测标准差(SEP)分别为0.60%、0.75%和0.18mmol/10g;在线仪器检测酒醅水分、淀粉、酸度的平均误差分别为-0.75%、0.48%、0.29mmol/10g,检测水分、淀粉、酸度的SEP分别为0.66%、0.97%、0.22mmol/10g。相比台式近红外分析结果,在线结果的平均误差及SEP均有所放大,但在线分析的准确度能满足酒醅在线配粮控制的精度要求。 相似文献
110.
由于镉在环境中具有高毒性和生物蓄积性,对人体和环境会产生巨大的危害,因而测定其在环境中的浓度是十分必要的。本研究基于镉(Ⅱ)-蛋白质-刚果红体系的共振瑞利散射和共振非线性散射光谱建立了测定环境水样中微量镉(Ⅱ)的新方法。在pH=4的BR缓冲溶液中,镉(Ⅱ)与牛血清白蛋白溶液及刚果红溶液反应生成三元离子缔合络合物,使该体系中的共振瑞利散射(RRS)、二级散射(SOS)和倍频散射(FDS)信号明显增强,其最大散射波长分别位于波长560nm(RRS)、690nm(SOS)和352nm(FDS)处。在优化的实验条件下,ΔI与镉(Ⅱ)浓度在一定范围内呈现良好的线性关系,检出限分别为0.31μg/L(RRS)、0.29μg/L(SOS)、0.34μg/L(FDS)。将该方法用于实验室废水、涪江河水和农夫山泉中镉(Ⅱ)的测定,水样中镉(Ⅱ)的回收率在93.2%~107.7%之间,相对标准偏差在0.8%~3.1%之间,取得了较理想的结果。 相似文献