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192 results on '"Wang Gaosheng"'

1. Development of Smart Oil and Gas Fields with Multi-energy Synergy of Wind, Solar, Geothermal, and Energy Storage

Author

Wang Tianyu, Li Gensheng, Song Xianzhi, Wang Haizhu, Wang Gaosheng, and Liu Zihao

Subjects
smart oil and gas fields, new energy resources, multi-energy synergy, wind-solar-geothermal-energy storage, low-carbon energy, Engineering (General). Civil engineering (General), TA1-2040
Abstract

In the context of carbon peaking and carbon neutrality as well as the construction of a new energy system, the integrated development and intelligent regulation of fossil energy with new energy resources such as wind, solar, and geothermal energy has become a new pattern for the future energy system. The oil and gas industry is undergoing digital and intelligent transformation, and the development of smart oil and gas fields will reduce exploration and development costs and increase social and economic benefits. This study elucidates the concept and implications of smart oil and gas fields with multi-energy synergy and the critical role of new smart oil and gas fields in enhancing oil and gas reserves and production and in promoting the green, low-carbon, and intelligent transformation of the oil and gas industry. It reviews the current development status of the wind-solar-geothermal-energy storage multi-energy synergy system, the integration of oil and gas fields with the multi-energy synergy system, and the smart oil and gas fields. The study also identifies the challenges and key issues faced by the development of smart oil and gas fields in China. It summarizes future scenarios for smart oil and gas fields with multi-energy synergy: (1) utilization of green electricity, (2) new geothermal systems for thermal recovery of abandoned heavy oil reservoirs, (3) production optimization of oil well clusters based on wind/solar power microgrids, (4) in-situ conversion of natural gas and utilization of associated gas for power generation, (5) comprehensive energy management systems for efficient and low-carbon oil and gas production, and (6) intelligent collaboration and optimization of electricity, geothermal energy, and hydrogen energy storage. The core output of the construction of smart oil and gas fields with multi-energy synergy is low-carbon oil fields and super energy basins. Under the premise of ensuring oil and gas production, future development should focus on supplementing fundamental shortcomings, increasing technological advantages, strengthening application capabilities, and achieving independence, thereby achieving breakthroughs in key core technologies to form practical development solutions.

Published
2024
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2. Design and implementation of face attribute recognition system

Author

Wang Gaosheng

Subjects
deep learning, convolutional neural network, face detection, face attributes recognition, application system development, Electronics, TK7800-8360
Abstract

At present, accurate face recognition has been widely used in real scenes, such as monitoring, access control, etc., and recognition only for face attributes is also widely used in entertainment, security and social media. Therefore, the paper is based on deep convolutional neural network MTCNN for face detection and Resnet50 network for face attribute recognition. At the same time, it uses Python language to design and develop a system that implements face attribute recognition application interface. Through data experiments, the system uses a face attribute recognition model based on deep convolutional neural networks to achieve 97.32%, 71.64% and 92.13% accuracy in identifying gender, age and ethnic attributes, respectively. At the same time, the face attribute recognition application system can run stably and has certain application value.

Published
2021
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3. Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning

Author

Liu, Peipei, Wang, Gaosheng, Tong, Ying, Liang, Jian, Ding, Zhenquan, and Zhu, Hongsong

Subjects
Computer Science - Computation and Language
Abstract

Few-shot named entity recognition can identify new types of named entities based on a few labeled examples. Previous methods employing token-level or span-level metric learning suffer from the computational burden and a large number of negative sample spans. In this paper, we propose the Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning (MsFNER), which splits the general NER into two stages: entity-span detection and entity classification. There are 3 processes for introducing MsFNER: training, finetuning, and inference. In the training process, we train and get the best entity-span detection model and the entity classification model separately on the source domain using meta-learning, where we create a contrastive learning module to enhance entity representations for entity classification. During finetuning, we finetune the both models on the support dataset of target domain. In the inference process, for the unlabeled data, we first detect the entity-spans, then the entity-spans are jointly determined by the entity classification model and the KNN. We conduct experiments on the open FewNERD dataset and the results demonstrate the advance of MsFNER.

Published
2024

5. SeHBPL: Behavioral Semantics-Based Patch Presence Test for Binaries

Author

Huang, Jintao, Wang, Gaosheng, Shi, Zhiqiang, Lv, Fei, Zhang, Weidong, Lv, Shichao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hermanns, Holger, editor, Sun, Jun, editor, and Bu, Lei, editor

Published
2024
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6. Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media

Author

Liu, Peipei, Wang, Gaosheng, Li, Hong, Liu, Jie, Ren, Yimo, Zhu, Hongsong, and Sun, Limin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Named Entity Recognition (NER) on social media refers to discovering and classifying entities from unstructured free-form content, and it plays an important role for various applications such as intention understanding and user recommendation. With social media posts tending to be multimodal, Multimodal Named Entity Recognition (MNER) for the text with its accompanying image is attracting more and more attention since some textual components can only be understood in combination with visual information. However, there are two drawbacks in existing approaches: 1) Meanings of the text and its accompanying image do not match always, so the text information still plays a major role. However, social media posts are usually shorter and more informal compared with other normal contents, which easily causes incomplete semantic description and the data sparsity problem. 2) Although the visual representations of whole images or objects are already used, existing methods ignore either fine-grained semantic correspondence between objects in images and words in text or the objective fact that there are misleading objects or no objects in some images. In this work, we solve the above two problems by introducing the multi-granularity cross-modality representation learning. To resolve the first problem, we enhance the representation by semantic augmentation for each word in text. As for the second issue, we perform the cross-modality semantic interaction between text and vision at the different vision granularity to get the most effective multimodal guidance representation for every word. Experiments show that our proposed approach can achieve the SOTA or approximate SOTA performance on two benchmark datasets of tweets. The code, data and the best performing models are available at https://github.com/LiuPeiP-CS/IIE4MNER, Comment: We have reconducted experiments of the paper, but found that there were fatal errors in our datasets leading to the wrong results and analyses. Therefore, we have to withdraw the paper to ensure the authenticity of science. We are very sorry

Published
2022

11. CSEDesc: CyberSecurity Event Detection with Event Description

Author

Wang, Gaosheng, Liu, Peipei, Huang, Jintao, Si, Shuaizong, Zhu, Hongsong, Sun, Limin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
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