Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： User perspective taking is a critical way in which entrepreneurs can identify opportunities and cope with market competition. However, existing studies have ignored its modes, formation mechanism and impact on opportunity belief performance. Considering these issues, first, by introducing structural alignment theory and the attentional engagement model, we identify and deconstruct the modes of user perspective taking from the two perspectives of "the main characteristics of the thinking process" and "the immersion degree of user perspective taking", and these four modes are absorptive, inductive, heuristic and analytical user perspective taking. Second, based on the dual perspective of the “individual-user”, we refine the influencing factors of user perspective taking, which include user-related prior knowledge, flexible role orientation, cognitive complexity, and the uncertainty and fragmentation of user needs. Then, we analyze the positive effects of these factors on the formation of user perspective taking. Third, taking the formation speed and innovativeness of opportunity beliefs as performance indicators, we explain the impact of the modes of user perspective taking on opportunity belief formation performance. This research finds that absorptive and inductive user perspective taking have positive effects on opportunity belief formation speed, while heuristic and analytical user perspective taking have negative effects on opportunity belief formation speed. Moreover, inductive and analytical user perspective taking have positive effects on opportunity belief innovativeness, while absorption and heuristic user perspective taking have negative effects on opportunity belief innovativeness. Finally, we examine the moderating effects of cognitive management strategies involving adaptation to new information environments and the invocation of the entrepreneur’s own knowledge structure on the modes of user perspective taking and opportunity belief formation performance. This research finds that a cognitive management strategy that adapts to a new information environment strengthens the positive effects of absorptive and inductive user perspective taking on opportunity belief formation speed and weakens the negative effects of heuristic and analytical user perspective taking on opportunity belief formation speed. Moreover, a cognitive management strategy that adapts to a new information environment strengthens the positive effects of inductive and analytical user perspective taking on opportunity belief innovativeness, while the moderating effect of this type of strategy on the relationship between absorptive and heuristic user perspective taking and opportunity belief innovativeness is not significant. A cognitive management strategy that invokes the entrepreneur’s own knowledge structure weakens the negative effects of absorptive and heuristic user perspective taking on opportunity belief innovativeness and strengthens the positive effects of inductive and analytical user perspective taking on opportunity belief innovativeness, while the moderating effect of this type of strategy on the relationship between absorptive, heuristic, inductive and analytical user perspective taking and opportunity belief formation speed is not significant. This study enriches the understanding of user perspective taking and deeply explores the formation mechanism of user perspective taking and its impact on opportunity beliefs. The theoretical advancement of this research mainly relates to the following aspects. First, the identification and deconstruction of user perspective taking modes have certain reference significance for promoting the operational development of this vague concept. Second, the clarification of the influencing factors of user perspective taking lays a foundation for subsequent research on the antecedents of user perspective taking. Third, this study clarifies the impact of the modes of user perspective taking on opportunity belief formation performance and introduces two different cognitive management strategies that have certain enlightening significance regarding the cognitive process and the boundary conditions of user perspective taking. Moreover, it promotes the development of research related to user perspective taking and opportunity belief. Regarding the practical significance of this research, entrepreneurs can employ user perspective taking to form opportunity beliefs and identify opportunities. On the one hand, entrepreneurs should be good at distinguishing between the characteristics of different user perspective-taking modes and adopt appropriate user perspective-taking modes according to different conditions. On the other hand, entrepreneurs should actively control the user perspective-taking process and make rational use of two different cognitive management strategies to eliminate the negative impact of "cognitive imbalance".
Subjects: Management Science >> Other Disciplines of Management Science submitted time 2022-07-20
Abstract： User perspective taking is a critical way in which entrepreneurs can identify opportunities and cope with market competition. However, existing studies have ignored its modes, formation mechanism and impact on opportunity belief performance. Considering these issues, we deconstruct the modes of user perspective taking based on structural alignment theory and the attentional engagement model, and these modes include absorptive, inductive, heuristic and analytical user perspective taking. Then, from the dual perspective of the “individual-user”, we explore the positive effects of user-related prior knowledge, flexible role orientation, cognitive complexity, and the uncertainty and fragmentation of user needs on the formation of user perspective taking. Finally, taking the formation speed and innovativeness of opportunity beliefs as performance indicators, we explain the impact of the modes of user perspective taking on opportunity belief formation performance and examine the moderating effects of cognitive management strategies involving adaptation to new information environments and the invocation of the entrepreneur’s own knowledge structure on the modes of user perspective taking and opportunity belief formation performance. This study enriches the connotation of user perspective taking, extends the explanatory scope of structural alignment theory and the attentional engagement model, and provides a reference that can guide entrepreneurs to better identify opportunities by employing user perspective taking. It is of great significance for entrepreneurs to think about and understand users.
Peer Review Status:
Awaiting Review
Subjects: Engineering and technical science >> Optical Engineering submitted time 2021-10-21
Abstract： This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development. Firstly, we introduced the problem of internal time misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior is proposed to calibrate the time difference between the laser and MEMS mirror, which can be calculated automatically without any artificial participation or specially designed cooperation target. Finally, actual experiments on MEMS LiDARs are implemented to demonstrate the effectiveness of the proposed method. It should be noted that the calibration can be implemented in a simple laboratory environment without any ranging equipment and artificial participation, which greatly accelerate the progress of research and development in practical applications."
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Software submitted time 2021-03-01
Abstract： " 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.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2020-08-26
Abstract： 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) "
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2020-03-06
Abstract： This paper proposes a self-supervised low light image enhancement method based on deep learning. Inspired by information entropy theory and Retinex model, we proposed a maximum entropy based Retinex model. With this model, a very simple network can separate the illumination and reflectance, and the network can be trained with low light images only. We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning. Our model is very simple and does not rely on any well-designed data set (even one low light image can complete the training). The network only needs minute-level training to achieve image enhancement. It can be proved through experiments that the proposed method has reached the state-of-the-art in terms of processing speed and effect. "
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-12-13 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the single description and the noise sensitive problems of the local binary pattern(LBP) , a multi-scale adaptive threshold local ternary pattern(MSALTP) algorithm is proposed. The algorithm first enlarges the original images . Secondly, divides the images into several regions equally and calculates the mean value of the pixels. Then calculates the deviation between the center and the mean pixel value of each region. Finally, extract the ALTP features and the resulting statistical features histograms are used to classify the images. Experiments show that the proposed algorithm recognition rates are higher than the current anti-noise algorithms under different noise.
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