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| 1 | Optimization Strategy Based on Deep Reinforcement Learning for Home Energy Management显示文摘With the development of a smart grid and smart home,massive amounts of data can be made available,providing the basis for algorithm training in artificial intelligence applications.These continuous improving conditions are expected to enable the home energy management system(HEMS)to cope with the increasing complexities and uncertainties in the enduser side of the power grid system.In this paper,a home energy management optimization strategy is proposed based on deep Q-learning(DQN)and double deep Q-learning(DDQN)to perform scheduling of home energy appliances.The applied algorithms are model-free and can help the customers reduce electricity consumption by taking a series of actions in response to a dynamic environment.In the test,the DDQN is more appropriate for minimizing the cost in a HEMS compared to DQN.In the process of method implementation,the generalization and reward setting of the algorithms are discussed and analyzed in detail.The results of this method are compared with those of Particle Swarm Optimization(PSO)to validate the performance of the proposed algorithm.The effectiveness of applied data-driven methods is validated by using a real-world database combined with the household energy storage model. | Yuankun Liu Dongxia Zhang Hoay Beng Gooi | 2020 | CSEE Journal of Power and Energy Systems2020，6，3： | 9 |
| 2 | Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation显示文摘 | Liwu Xu Yuanzheng Li Kaicheng Li Gooi Hoay Beng Zhiqiang Jiang Chao Wang Nian Liu | 2018 | Journal of Bionic Engineering2018，15，4： | 4 |
| 3 | Hydrophobic protein in colorectal cancer in relation to tumor stages and grades显示文摘AIM: To identify differentially expressed hydrophobic proteins in colorectal cancer. METHODS: Eighteen pairs of colorectal cancerous tissues in addition to tissues from normal mucosa were analysed. Hydrophobic proteins were extracted from the tissues, separated using 2-D gel electrophoresis and analysed using Liquid Chromatography Tandem Mass Spectrometry (LC/MS/MS). Statistical analysis of the proteins was carried out in order to determine the significance of each protein to colorectal cancer (CRC) and also their relation to CRC stages, grades and patients’ gender. RESULTS: Thirteen differentially expressed proteins which were expressed abundantly in either cancerous or normal tissues were identified. A number of these proteins were found to relate strongly with a particular stage or grade of CRC. In addition, the association of these proteins with patient gender also appeared to be significant.CONCLUSION: Stomatin-like protein 2 was found to be a promising biomarker for CRC, especially in female patients. The differentially expressed proteins identified were associated with CRC and may act as drug target candidates. | Lay-Chin Yeoh Chee-Keat Loh Boon-Hui Gooi Manjit Singh Lay-Harn Gam | 2010 | World Journal of Gastroenterology2010，16，22： | 3 |
| 4 | Web-based SCADA display systems(WSDS) for access via intemet显示文摘 | Qiu B Gooi H B | 2000 | IEEE Trans on Power Systems2000，15，2： | 2 |
| 5 | Data-driven Decision-making Strategies for Electricity Retailers:A Deep Reinforcement Learning Approach显示文摘With the continuous development of the electricity market,the electricity retailers,as the intermediaries between producers and consumers,have emerged in some of the liberalized electricity markets.Meanwhile,the electricity retailer faces many increasingly significant challenges from the complexities and uncertainties in both the supply and consumption sides.This paper applies a data-driven decision-making strategy via Advantage Actor-Critic(A2C)and Deep Q-Learning(DQN)for the electricity retailers.The retailers’profits and consumers’costs are both taken into account.This study verifies that the applied data-driven methods can handle the decision-making problem as well as promote the profitability of retailers in the electricity market.Furthermore,A2C is more appropriate than DQN in our simulation.The effectiveness of the applied data-driven methods is validated by using real-world data. | Yuankun Liu Dongxia Zhang Hoay Beng Gooi | 2021 | CSEE Journal of Power and Energy Systems2021，7，2： | 2 |
| 6 | Dynamic economic dispatch:feasible and optimal solutions显示文摘 | Han X S Gooi H B Kirschen D S | 2001 | IEEE Transactions on Power Systems2001，3，1： | 1 |
| 7 | Internet-based SCADA display system显示文摘 | QIU Bin GOOI H B | 2002 | IEEE Trans on Power Systems2002，15，1： | 1 |
| 8 | Sizing of energystorage for microgrids显示文摘 | Chen S X Gooi H B Wang M Q | 2012 | IEEE Trans on Smart Grid2012，1，3： | 1 |
| 9 | Optimal scheduling of spinning reserve 显示文摘 | Gooi H B Mendes D P Be11 K R W | 1999 | IEEE Transactions on Power Systems1999，14，4： | 1 |
| 10 | Dynamic economic dispatch: feasible and optimal solutions显示文摘 | Han X S Gooi H B Daniel S K | 2001 | IEEE Trans on Power Apparatus and Systems2001，16，1： | 1 |
| 11 | Chemometrics of differentially expressed proteins from colorectal cancer patients显示文摘AIM:To evaluate the usefulness of differentially expressed proteins from colorectal cancer (CRC) tissues for differentiating cancer and normal tissues.METHODS:A Proteomic approach was used to identify the differentially expressed proteins between CRC and normal tissues.The proteins were extracted using Tris buffer and thiourea lysis buffer (TLB) for extraction of aqueous soluble and membrane-associated proteins,respectively.Chemometrics,namely principal component analysis (PCA) and linear discriminant analysis (LDA),were used to assess the usefulness of these proteins for identifying the cancerous state of tissues.RESULTS:Differentially expressed proteins identified were 37 aqueous soluble proteins in Tris extracts and 24 membrane-associated proteins in TLB extracts.Based on the protein spots intensity on 2D-gel images,PCA by applying an eigenvalue > 1 was successfully used to reduce the number of principal components (PCs) into 12 and seven PCs for Tris and TLB extracts,respectively,and subsequently six PCs,respectively from both the extracts were used for LDA.The LDA classification for Tris extract showed 82.7% of original samples were correctly classified,whereas 82.7% were correctly classified for the cross-validated samples.The LDA for TLB extract showed that 78.8% of original samples and 71.2% of the cross-validated samples were correctly classified.CONCLUSION:The classification of CRC tissues by PCA and LDA provided a promising distinction between normal and cancer types.These methods can possibly be used for identification of potential biomarkers among the differentially expressed proteins identified. | Lay-Chin Yeoh Saravanan Dharmaraj Boon-Hui Gooi Manjit Singh Lay-Harn Gam | 2011 | World Journal of Gastroenterology2011，17，16： | 1 |
| 12 | Algorithms for automatic generation of one-line diagrams显示文摘 | Ong Y S Gooi H B Chan C K | 2000 | IEEE Proceedings of Generation Transmission and Distribution2000，147，5： | 1 |
| 13 | Sizing of energy storagefor microgrids 显示文摘 | Chen S X Gooi H B Wang M Q | 2012 | IEEE Transactions on Smart Grid2012，3，1： | 1 |
| 14 | Sizing of energy storage system for microgrid显示文摘 | Chen S X Gooi H B | 2012 | IEEE Transactions on Smart Grid2012，3，1： | 1 |
| 15 | Web-based SCADA display systems (WSDS) for access via internet显示文摘 | Qiu B Gooi HB | 2000 | IEEE Trans on Power Systems2000，15，2： | 1 |
| 16 | Molecular mechanisms in coupling ofbone formation to resorption显示文摘 | Martin T Gooi J H Sims N A | 2009 | Crit Rev Eukaryot Gene Expr2009，19，1： | 1 |
| 17 | Spinning reserve estimation in microgTids 显示文摘 | WANG M Q GOOI H B | 2011 | IEEE Trans on Power Systems2011，26，3： | 1 |
| 18 | Dynamic economic dispatch:feasibleand optimal solutions显示文摘 | Han X S Gooi H B Kirschen D S | 2001 | IEEE Trans on Power Systems2001，16，1： | 1 |
| 19 | Sizing of energy storage for micro-grids显示文摘 | CHEN S X GOOI H B WANG M Q | 2012 | IEEE Transactions on Smart Grid2012，3，1： | 1 |
| 20 | Unit commitment –a fuzzy mixed linear programming solution显示文摘 | Venkatesh B Jamtsho T Gooi H B | 2007 | Generation Transmission & Distribution IET2007，5，1： | 1 |
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