分类： 管理学 >> 人力资源开发与管理 提交时间： 2023-11-08
摘要： 以往研究更多关注资质过剩的消极面, 而且相对忽视了管理层的资质过剩现象。本文基于自我调节理论和基于过程的团队创新整合理论, 探讨了资质过剩的领导何时以及如何提升团队创造力。通过对106个护理团队数据的分析, 结果发现: 领导感知到的团队能力调节了领导资质过剩感通过领导鼓励创新和团队创新过程投入对团队创造力的间接效应: 当团队能力较高时, 领导鼓励创新和团队创新过程投入对领导资质过剩感与团队创造力的链式中介效应更强。本文通过对领导资质过剩现象的关注, 揭示了其对团队创造力产生积极影响的边界条件和过程, 为资质过剩研究开辟了新的研究视角和思路。
分类： 管理学 >> 管理工程 提交时间： 2023-08-10
摘要： [目的] 为全面构建根治农民工欠薪工作的新格局，倍增欠薪智慧治理的能力与效果。[方法] 分析农民工欠薪治理现状与欠薪智慧治理监管困境，在社会共治框架下，以领域知识工程模式为顶层设计方法，[结论]提出农民工欠薪智慧治理顶层设计的21231整体布局，包括两大制度、一个目标、两大能力、三个基础与一个平台。
分类： 工程与技术科学 >> 光学工程 提交时间： 2021-10-21
摘要： 针对MEMS激光雷达在研发过程中的内部时间同步问题，提出了一种简单的自标定方法。首先，我们介绍了MEMS激光雷达的内部时间不对齐问题。在此基础上，提出了一种鲁棒的最小垂直梯度（MVG）先验算法，用于标定激光与MEMS反射镜之间的时间差，该时间差可以自动计算，无需任何人工参与或专门设计的合作目标。最后，在MEMS激光雷达上进行了实际实验，验证了该方法的有效性。需要注意的是，校准可以在简单的实验室环境中进行，无需任何测距设备和人工参与，这大大加快了实际应用中的研发进度。
分类： 计算机科学 >> 计算机软件 提交时间： 2021-03-01
摘要： This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed. In the ME-Retinex model, a new constraint on the reflectance image is introduced that the maximum channel of the reflectance image conforms to the maximum channel of the low light image and its entropy should be the largest, which converts the decomposition of reflectance and illumination in Retinex model to a non-ill-conditioned problem and allows the ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net are carefully formulated to separate the noises and details during training, and they are based on the idea that, if noises are independently distributed, after the processing of smoothing filters (\eg mean filter), the gradient of the noise part should be smaller than the gradient of the detail part. It can be proved qualitatively and quantitatively through experiments that the proposed method is efficient.
分类： 计算机科学 >> 计算机应用技术 提交时间： 2020-08-26
摘要： Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion. It should be noted that during the training process, any paired images with different exposure time can be used for training, and there is no need to carefully select the supervised images which will save a lot. In addition, it takes less than 20 ms to process a color image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness. (Code will be available at https://github.com/hitzhangyu/image-enhancement-with-denoise)
分类： 心理学 >> 生理心理学 分类： 语言学及应用语言学 >> 语言学及应用语言学 提交时间： 2020-06-19
摘要： 本文梳理了国际期刊发表的使用事件相关电位（ERP）技术研究人脑开展预测性句子加工所取得的主要成果和重要突破。本文从心理语言学对句子预测的研究逻辑为切入点，接着分别回顾通过N400和前侧正波(frontal positivity)两个ERP效应揭示词形预测和语义预测这两个预测性句子加工主要操作的研究里程碑，并进而总结勾勒出预测性句子加工理论模型。最后，本文指出了现有研究的局限性和未来该课题潜在的方向。
分类： 计算机科学 >> 计算机应用技术 提交时间： 2020-04-14
摘要： Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio parameter must be chosen manually to complete the enhancement pipeline. In this work, we propose an adaptive low-light raw image enhancement network to avoid parameter-handcrafting and to improve image quality. The proposed method can be divided into two sub-models: Brightness Prediction (BP) and Exposure Shifting (ES). The former is designed to control the brightness of the resulting image by estimating a guideline exposure time t 1 . The latter learns to approximate an exposure-shifting operator ES, converting a low-light image with real exposure time t 0 to a noise-free image with guideline exposure time t 1 . Additionally, structural similarity (SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image quality, and a new Campus Image Dataset (CID) is proposed to overcome the limitations of the existing datasets and to supervise the training of the proposed model. In quantitative tests, it is shown that the proposed method has the lowest Noise Level Estimation (NLE) score compared with BM3D-based low-light algorithms, suggesting a superior denoising performance. Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene. Lastly, the potential application in video processing is briefly discussed.
分类： 计算机科学 >> 计算机科学技术其他学科 提交时间： 2020-03-06
摘要： 本文提出了一种基于深度学习的自监督低照度图像增强方法。受信息熵理论和Retinex模型的启发，我们提出了一种基于信息熵最大的Retinex模型。利用该模型，一个非常简单的网络可以将照度图和反射图分离开来，且仅用低照度图像就可以进行训练。为了实现自监督学习，我们在模型中引入了一个约束条件：反射图的最大值通道与低照度图像的最大值通道一致，且其熵最大。我们的模型非常简单，不依赖任何精心设计的数据集（即使是一张低照度图像也能完成网络的训练），网络仅需进行分钟级的训练即可实现图像增强。实验证明，该方法在处理速度和效果上均达到了当前最新水平。
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