Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling

Li, Yuanman; Zhou, Jiantao; Zheng, Xianwei; Jing-zhen, Tian; Tang, Yuan Yan

doi:10.1109/cvpr.2019.00892

Cited by 22 publications

(13 citation statements)

References 38 publications

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“…The recommendation and one-click collection functions are also provided in the system. In the future, we will design more applications by incorporating other fields [34][35][36][37] based on the system.…”

Section: Discussionmentioning

confidence: 99%

ColorEmo: Color Hunt with Affective Words

Wang

Song

Chen

et al. 2021

Wireless Communications and Mobile Computing

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Color is a main channel to express emotions of an image. Selecting a good color scheme with color harmony and proper emotional expression is very important in many fields, such as design. There are many color palette tools to help users search, create, edit, save, and share color themes, but few tools are from the perspective of emotional expression and explicitly address the emotional effects of colors. In this paper, a color hunt system with affective words called ColorEmo is developed. Multiple types of input, including affective words, affective categories in image-scale space, and main colors, are allowed for users with accurate emotional description. Based on the dataset with 428,924 color themes, the system provides rich candidates. The system is designed for user research experiments. The user-friendly interactions are offered for easy color modifications. The affective matching and color harmony are evaluated in real time while changing colors. This system can be used in many scenarios of designs and applications.

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Section: Discussionmentioning

confidence: 99%

ColorEmo: Color Hunt with Affective Words

Wang

Song

Chen

et al. 2021

Wireless Communications and Mobile Computing

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“…μ WiFi for WiFi AP 11,13,14,16,12, and 15 are set to 2.39 and 1.43 (ms), respectively. These average packet arrival intervals correspond to LTE subframe numbers 3 and 5, respectively.…”

Section: Existing Problems and The Enhanced Proposed Schemementioning

confidence: 99%

“…The goal is to achieve fair medium access by giving more transmission opportunities to WiFi systems. Based on [11] and recent advances in learning techniques [12][13][14], a Q-learning-based muting period selection scheme was proposed for fair LTE-WiFi coexistence in [15]. However, it focuses on maximizing the LTE throughput and can only work in single channel scenario.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum

Kishimoto

Wang

Umehira

2021

Wireless Communications and Mobile Computing

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In recent years, to cope with the rapid growth in mobile data traffic, increasing the capacity of cellular networks is receiving more and more attention. To this end, offloading the current LTE-advanced or 5G system’s data traffic from licensed spectrum to the unlicensed spectrum that is used by WiFi systems, i.e., LTE-Licensed-Assisted-Access (LTE-LAA), has been extensively investigated. In the current LTE-LAA system, a Listen-Before-Talk (LBT) approach is implemented, which requires the LTE user also perform carrier sense before the transmission. However, fair LTE-WiFi coexistence is still hard to guarantee due to their unbalanced frame sizes and traffic loads. In the LTE-LAA system, the optimal channel selection and subframe number adjustment are the keys to realize efficient spectrum utilization and fair system coexistence. To this end, in this paper, we propose a reinforcement learning-based joint channel/subframe selection scheme for LTE-LAA. The proposed approach is implemented at the LTE access points with zero knowledge of the WiFi systems. The results of extensive simulations verify that the proposed approach can significantly improve the fairness and packet loss rate compared with baseline schemes.

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“…ey have developed advanced smart service systems and platforms and built an ecological smart logistics supply chain. Some intelligent algorithms such as machine learning methods have been developed [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Impact of Logistics Development on Economic Growth: An Empirical Research from Guangdong Province in China

Chen

2021

Complexity

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This paper evaluates the impact of the development of the logistics industry on economic growth based on panel data of 21 cities in Guangdong Province in China from 2007 to 2019. Using the overall entropy method, the comprehensive development index of the logistics industry is constructed to capture the performance of the logistics industry. On this basis, by using Moran’s index, we measure the spatial agglomeration effect of the logistics industry. Furthermore, direct and spatial spillover effects are also obtained. The estimation results of the spatial Durbin model (SDM) reveal that the elasticity coefficient of the effect of the comprehensive development level of the logistics industry on economic growth is 0.2590, which is significant at the 1% level. In addition, the coefficient of the direct effect of the logistics industry on local economic growth is 0.4074, and the coefficient of the spatial spillover effect on the economic growth of the surrounding areas is 0.3596, which are both significant at the 1% level. These results indicate that the development of the logistics industry can not only improve local economic growth but also promote the economic growth of the surrounding regions.

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Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling

Cited by 22 publications

References 38 publications

ColorEmo: Color Hunt with Affective Words

ColorEmo: Color Hunt with Affective Words

Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum

Impact of Logistics Development on Economic Growth: An Empirical Research from Guangdong Province in China

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