ORCID Profile
0000-0003-2774-0511
Current Organisation
University of Sydney
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: IEEE
Date: 09-2019
Publisher: Hindawi Limited
Date: 13-09-2021
DOI: 10.1155/2021/4011190
Abstract: With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are ided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.
Publisher: IEEE
Date: 08-2020
Publisher: IEEE
Date: 12-03-2021
Publisher: Springer Science and Business Media LLC
Date: 18-09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Hindawi Limited
Date: 12-06-2021
DOI: 10.1155/2021/5552626
Abstract: Vehicle to Grid (V2G) refers to the optimal management of the charging and discharging behavior of electric vehicles through reasonable strategies and advanced communication. In the process of interaction, there are three stakeholders: the power grid, operators (charging stations), and EV users. In real life, the impact of peak-valley difference caused a lot of power loss when charging. At the same time, the loss of current is also a loss for power grid companies and EV users. In this paper, we propose a multiobjective optimization method to reduce the current loss and determine the relationship between the parameters and the objective function and constraints. This optimization method uses a genetic algorithm for multiobjective optimization. Through the analysis of the number of vehicles and load curve of AC class I and AC class II electric vehicles before and after optimization in each period, we found that the charging load of electric vehicles played a role of valley filling in the low valley price stage and played a peak-cutting role in a peak price period.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Elsevier BV
Date: 03-2022
Publisher: Association for Computing Machinery (ACM)
Date: 09-05-2023
DOI: 10.1145/3563390
Abstract: With the rapid development of information technology, image and text data have increased dramatically. Image and text matching techniques enable computers to understand information from both visual and text modalities and match them based on semantic content. Existing methods focus on visual and textual object co-occurrence statistics and learning coarse-level associations. However, the lack of intramodal semantic inference leads to the failure of fine-level association between modalities. Scene graphs can capture the interactions between visual and textual objects and model intramodal semantic associations, which are crucial for the understanding of scenes contained in images and text. In this article, we propose a novel scene graph semantic inference network (SGSIN) for image and text matching that effectively learns fine-level semantic information in vision and text to facilitate bridging cross-modal discrepancies. Specifically, we design two matching modules and construct scene graphs within each matching module for aggregating neighborhood information to refine the semantic representation of each object and achieve fine-level alignment of visual and textual modalities. We perform extended experiments in Flickr30K and MSCOCO and achieve state-of-the-art results, which validate the advantages of our proposed approach.
No related grants have been discovered for Jiaming Pei.