Institution
Hong Kong Polytechnic University
Education•Hong Kong, China•
About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Computer science & Tourism. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.
Papers published on a yearly basis
Papers
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08 Jul 2009TL;DR: Compared with other PSO algorithms, the comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness.
Abstract: This paper proposes an orthogonal learning particle swarm optimization (OLPSO) by designing an orthogonal learning (OL) strategy through the orthogonal experimental design (OED) method. The OL strategy takes the dimensions of the problem as the orthogonal experimental factors. The levels of each dimension (factor) are the two choices of the personal best position and the neighborhood's best position. By orthogonally combining the two learning exemplars, the useful information can be discovered, preserved and utilized to construct an efficient exemplar to guide the particle to fly in a more promising direction towards the global optimum. The effectiveness and efficiency of the OL strategy is demonstrated on a set of benchmark functions by comparing the PSOs with and without OL strategy. The OL strategy improves the PSO algorithm in terms of higher quality solution and faster convergence speed.
293 citations
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01 Feb 2020TL;DR: Key design issues, methodologies, and hardware platforms are introduced, including edge-assisted perception, mapping, and localization for intelligent IoV, and typical use cases for intelligent vehicles are illustrated.
Abstract: The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications that will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend on communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and cloud computing will not be sufficient due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. EIS will provide not only low-latency content delivery and computation services but also localized data acquisition, aggregation, and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies, and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open-research problems are identified.
293 citations
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TL;DR: In this article, the translocation ratio of Pb from vetiver roots to shoots was significantly increased after 5.0 mmol EDTA kg−1 of soil application and nearly 126 mm of rainfall irrigation.
293 citations
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TL;DR: In this paper, a well-adherent surface of titanium oxide nanoparticles was produced on cellulose fibers at low temperature from an aqueous titania sol that was obtained via hydrolysis and condensation reactions of titanium isopropoxide in water.
292 citations
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20 Jun 2011TL;DR: This paper proposes a framework for both magnification and deblurring using only the original low-resolution image and its blurred version, and shows that when using a proper covariance function, the Gaussian process regression can perform soft clustering of pixels based on their local structures.
Abstract: In this paper we address the problem of producing a high-resolution image from a single low-resolution image without any external training set. We propose a framework for both magnification and deblurring using only the original low-resolution image and its blurred version. In our method, each pixel is predicted by its neighbors through the Gaussian process regression. We show that when using a proper covariance function, the Gaussian process regression can perform soft clustering of pixels based on their local structures. We further demonstrate that our algorithm can extract adequate information contained in a single low-resolution image to generate a high-resolution image with sharp edges, which is comparable to or even superior in quality to the performance of other edge-directed and example-based super-resolution algorithms. Experimental results also show that our approach maintains high-quality performance at large magnifications.
292 citations
Authors
Showing all 30115 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jing Wang | 184 | 4046 | 202769 |
Xiang Zhang | 154 | 1733 | 117576 |
Wei Zheng | 151 | 1929 | 120209 |
Rui Zhang | 151 | 2625 | 107917 |
Jian Yang | 142 | 1818 | 111166 |
Joseph Lau | 140 | 1048 | 99305 |
Yu Huang | 136 | 1492 | 89209 |
Dacheng Tao | 133 | 1362 | 68263 |
Chuan He | 130 | 584 | 66438 |
Lei Zhang | 130 | 2312 | 86950 |
Ming-Hsuan Yang | 127 | 635 | 75091 |
Chao Zhang | 127 | 3119 | 84711 |
Yuri S. Kivshar | 126 | 1845 | 79415 |
Bin Wang | 126 | 2226 | 74364 |
Chi-Ming Che | 121 | 1305 | 62800 |