Browsing by Author "Liu M"

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    A Blockchain Based Data Monitoring and Sharing Approach for Smart Grids
    (IEEE, 2019-11-11) Yang Y; Liu M; Zhou Q; Zhou H; Wang R
    With the development of science and technology, human beings cannot live without electricity. The introduction of smart grid systems brings new ideas to break the shackle of existing electricity systems. This paper proposes a mechanism with data monitoring and sharing capabilities based on the consortium blockchain, realizing comprehensive monitoring of smart devices, and promoting the effective sharing of electrical data in smart grids. When a smart device is out of order, the smart contract connected to it will be triggered, and the users can check the running status through the smart phone. This approach allows nodes in the consortium blockchain to request transactions, using the prepaid payment smart contract with time-lock script to protect the consumer right of request nodes. In addition, we use a (t, n) -threshold secret sharing scheme to realize multiparty sharing of electrical data. Paillier encryption arithmetic is used to guarantee the confidentiality of messages in node transaction.
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    Cooling demand reduction with nighttime natural ventilation to cool internal thermal mass under harmonic design-day weather conditions
    (Elsevier Ltd, 2025-02-01) Li M; Shen X; Wu W; Cetin K; Mcintyre F; Wang L; Ding L; Bishop D; Bellamy L; Liu M
    Cooling demand is steadily increasing across different climate zones due to global warming. A potential solution for cooling demand reduction is applying nighttime natural ventilation to cool internal thermal mass. However, a simplified and accurate modelling framework to assess the technique is still missing. The goal of the study is to build that framework integrated with a validated internal thermal mass model and apply the framework to quantify the cooling demand reduction potential in a space with different thermal mass and envelope configurations and in different climate zones. Results show that using Granite as internal thermal mass is three times more effective than concrete to reduce peak cooling load. Adding too much internal thermal mass can create adverse effects on cooling load reduction. The optimum thickness of internal thermal mass is between 28 and 45 mm. Envelope construction also has an influence on the performance of nighttime cooling. Applying the technique in buildings with lightweight structures reduces peak cooling load by 35.9% more than heavyweight structures. As heavyweight structures delay the release of the daily absorbed heat and cause higher indoor air temperatures at night. The two belts between the Tropic of Cancer and 60 degrees north latitude, and between the Tropic of Capricorn and 45 degrees south latitude are suitable for nighttime natural ventilation of internal thermal mass, achieving the annual cooling demand reduction above 1.25 kWh m−2. In Dessert climate zones, the technique exhibits an extraordinary potential to reduce cooling demand, up to 6.67 kWh m−2 per year.
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    Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing
    (MDPI (Basel, Switzerland), 2022-10-11) Liu H; Liu M; Li D; Zheng W; Yin L; Wang R; Song BC
    This paper surveys recent advances in pulse-coupled neural networks (PCNNs) and their applications in image processing. The PCNN is a neurology-inspired neural network model that aims to imitate the information analysis process of the biological cortex. In recent years, many PCNN-derived models have been developed. Research aims with respect to these models can be divided into three categories: (1) to reduce the number of manual parameters, (2) to achieve better real cortex imitation performance, and (3) to combine them with other methodologies. We provide a comprehensive and schematic review of these novel PCNN-derived models. Moreover, the PCNN has been widely used in the image processing field due to its outstanding information extraction ability. We review the recent applications of PCNN-derived models in image processing, providing a general framework for the state of the art and a better understanding of PCNNs with applications in image processing. In conclusion, PCNN models are developing rapidly, and it is projected that more applications of these novel emerging models will be seen in future.