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453 results on '"Chen, Yuxia"'

1. Chapter A Robotic Method to Insert Batt Insulation into Light-Frame Wood Wall for Panel Prefabrications

Author

Han, Xiao, Sanchez, Alejandra Hernandez, Yang, Cheng-Hsuan, and Chen, Yuxiang

Subjects
Robotic Building Prefabrication, Robotic Insertion, Light-frame Wood Construction, Robotic End-effector, Automation in Construction, Thermal Insulation, thema EDItEUR::U Computing and Information Technology
Abstract

Currently, industrial robot arms are trending in prefabricated building construction; however, a notable gap exists in established automated processes and related research specifically for the insertion of batt thermal insulation. The current method for accomplishing this task relies on manual insertion, which is labour-intensive for the workers and poses long-term health and safety concerns. This research presents an ongoing research project aimed at developing a feasible robotic process for the automated insertion of batt thermal insulation into prefabricated light-frame wood wall frames. This research focuses on the utilization of a single 6-degree-of-freedom robot arm for the insertion process, complimented by the design of a custom-built end-effector. The proposed robotic insertion process, named GLITPP, comprises of six major steps: (1) Grasp, (2) Lift, (3) Insert, (4) Tilt, (5) Push, and (6) Press. The GLITPP insertion process, along with the custom-built end-effector effectively mitigates the influence of the insulation’s nonlinear mechanical properties, while also taking collision avoidance into consideration. This ensures a tight-fitting insulation within the frame cavity, without visible gaps and deficiencies. The necessary physical operating parameters for the insertion process, such as angles, offset, and force requirements, are identified to ensure the precision, efficiency, and repeatability of insertion. A prototype of the designed end-effector is used to demonstrate and validate the robotic method, achieved a high success rate of 93.3%. The development of this research will further advance the complete automation of light-frame wood wall panel prefabrication, offering the industry a wider range of options for selecting thermal insulation for their processes

Published
2023
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2. Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation

Author

Chen, Yuxia, Fang, Pengcheng, Yu, Jianhui, Zhong, Xiaoling, Zhang, Xiaoming, and Li, Tianrui

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

High-resolution remote sensing (HRS) semantic segmentation extracts key objects from high-resolution coverage areas. However, objects of the same category within HRS images generally show significant differences in scale and shape across diverse geographical environments, making it difficult to fit the data distribution. Additionally, a complex background environment causes similar appearances of objects of different categories, which precipitates a substantial number of objects into misclassification as background. These issues make existing learning algorithms sub-optimal. In this work, we solve the above-mentioned problems by proposing a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs, which consists of a funnel module, a multi-branch module with stacks of information aggregation (IA) blocks, and a feature refinement module, sequentially, and Class-agnostic Edge Aware (CEA) loss. Specifically, we propose a funnel module to downsample, which reduces the computational cost, and extract high-resolution semantic information from the initial input image. Secondly, we downsample the processed feature images into multi-resolution branches incrementally to capture image features at different scales and apply IA blocks, which capture key latent information by leveraging attention mechanisms, for effective feature aggregation, distinguishing image features of the same class with variant scales and shapes. Finally, our feature refinement module integrate the CEA loss function, which disambiguates inter-class objects with similar shapes and increases the data distribution distance for correct predictions. With effective pre-training strategies, we demonstrated the superiority of Hi-ResNet over state-of-the-art methods on three HRS segmentation benchmarks.

Published
2023

36. Integrating GPT into the Journalism and Communication Industries: The Technological Logic, Ecosystem Reconstruction, and Ethical Challenges.

Author

Chen Yuxia

Subjects
GENERATIVE artificial intelligence, JOURNALISM, TECHNOLOGICAL innovations, DATA security, DATA privacy
Abstract

Generative AI, represented by GPT (Generative Pre-trained Transformer), is now leading the technological revolution and is reconstructing the journalism and communication industries’ ecosystems because of its powerful generative capacity and diverse range of outputs. While GPT is busy revolutionizing and innovating the production of news content, working patterns, and operation modes, it has also given rise to ethical concerns in regard to news authenticity, data security, humanistic values, and other related aspects. Therefore, it is imperative to initiate strategies and approaches, such as establishing a mechanism for verifying information authenticity, enhancing data security and privacy regulations, and instituting an ethical supervision and governance framework for AI, in order to facilitate the systematic advancement of AI-based news production while reinstating public trust. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Emotion recognition model based on CLSTM and channel attention mechanism

Author

Chen Yuxia, Wang Dan, and Wang Xiaoxi

Subjects
emotion recognition, eeg, cnn, lstm, attention mechanism, Information technology, T58.5-58.64
Abstract

In this paper, we propose an emotion recognition model based on convolutional neural network (CNN), long short term memory (LSTM) and channel attention mechanism, aiming at the low classification accuracy of machine learning methods and the uneven spatial distribution of electroencephalogram (EEG) electrodes. This model can effectively integrate the frequency, space and time information of EEG signals, and improve the accuracy of emotion recognition by adding channel attention mechanism after the last convolutional layer of the model. Firstly, construct a 4-dimensional structure representing EEG signals. Then, a CLSTM model structure combining CNN and LSTM is designed. CNN is used to extract frequency and spatial information from 4-dimensional input, and LSTM is used to extract time information. Finally, the channel attention module is added after the last convolutional layer of CLSTM model structure to allocate the weight of different electrodes. In this paper, an emotion recognition model based on CLSTM and channel attention mechanism was proposed from the perspective of integrating the frequency, space and time 3-dimensional information of EEG signals. The average classification accuracy of the model on SEED public data set reached 93.36%, which was significantly improved over the existing CNN and LSTM emotion recognition models.

Published
2022
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50. Smart controlled-release nanopesticides

Author

Dong, Jiangtao, primary, Chen, Yuxia, additional, Li, Xiaona, additional, Li, Tongtong, additional, Liu, Jincan, additional, Luan, Xingkun, additional, Li, Hang, additional, Wang, Chen, additional, and Du, Xuezhong, additional

Published
2023
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