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555 results on '"Yu Hongliang"'

1. The USTC-NERCSLIP Systems for The ICMC-ASR Challenge

Author

Wu, Minghui, Xu, Luzhen, Zhang, Jie, Tang, Haitao, Yue, Yanyan, Liao, Ruizhi, Zhao, Jintao, Zhang, Zhengzhe, Wang, Yichi, Yan, Haoyin, Yu, Hongliang, Ma, Tongle, Liu, Jiachen, Wu, Chongliang, Li, Yongchao, Zhang, Yanyong, Fang, Xin, and Zhang, Yue

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This report describes the submitted system to the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) challenge, which considers the ASR task with multi-speaker overlapping and Mandarin accent dynamics in the ICMC case. We implement the front-end speaker diarization using the self-supervised learning representation based multi-speaker embedding and beamforming using the speaker position, respectively. For ASR, we employ an iterative pseudo-label generation method based on fusion model to obtain text labels of unsupervised data. To mitigate the impact of accent, an Accent-ASR framework is proposed, which captures pronunciation-related accent features at a fine-grained level and linguistic information at a coarse-grained level. On the ICMC-ASR eval set, the proposed system achieves a CER of 13.16% on track 1 and a cpCER of 21.48% on track 2, which significantly outperforms the official baseline system and obtains the first rank on both tracks., Comment: Accepted at ICASSP 2024

Published
2024

7. Page-level Optimization of e-Commerce Item Recommendations

Author

Lo, Chieh, Yu, Hongliang, Yin, Xin, Shetty, Krutika, He, Changchen, Hu, Kathy, Platz, Justin, Ilardi, Adam, and Madhvanath, Sriganesh

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

The item details page (IDP) is a web page on an e-commerce website that provides information on a specific product or item listing. Just below the details of the item on this page, the buyer can usually find recommendations for other relevant items. These are typically in the form of a series of modules or carousels, with each module containing a set of recommended items. The selection and ordering of these item recommendation modules are intended to increase discover-ability of relevant items and encourage greater user engagement, while simultaneously showcasing diversity of inventory and satisfying other business objectives. Item recommendation modules on the IDP are often curated and statically configured for all customers, ignoring opportunities for personalization. In this paper, we present a scalable end-to-end production system to optimize the personalized selection and ordering of item recommendation modules on the IDP in real-time by utilizing deep neural networks. Through extensive offline experimentation and online A/B testing, we show that our proposed system achieves significantly higher click-through and conversion rates compared to other existing methods. In our online A/B test, our framework improved click-through rate by 2.48% and purchase-through rate by 7.34% over a static configuration., Comment: Accepted by RecSys 2021

Published
2021

25. Force Analysis and Experiment of Variable Stiffness Soft Actuator Based on Particle Jamming

Author

Jiang, Fengyou, Xu, Fengyu, Yu, Hongliang, Song, Yurong, Cao, Xudong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yu, Haibin, editor, Liu, Jinguo, editor, Liu, Lianqing, editor, Ju, Zhaojie, editor, Liu, Yuwang, editor, and Zhou, Dalin, editor

Published
2019
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37. Adversarial Framework for General Image Inpainting

Author

Huang, Wei, Yu, Hongliang, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Shi, Yong, editor, Fu, Haohuan, editor, Tian, Yingjie, editor, Krzhizhanovskaya, Valeria V., editor, Lees, Michael Harold, editor, Dongarra, Jack, editor, and Sloot, Peter M. A., editor

Published
2018
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40. Numerical Investigation of Different Combustion Models for Dual‐Fuel Engine Combustion Processes.

Author

Liu, Xicai, Wu, Wenfeng, Wang, Fubo, Wang, Zhengquan, Guo, Jia, and Yu, Hongliang

Subjects
DUAL-fuel engines, COMBUSTION, CARBON emissions, DIESEL motors
Abstract

The combustion process of a dual‐fuel engine was calculated by characteristic timescale model (CTM), eddy breakup model (EBM), and coherent flamelet model (CFM) to verify the accuracy of the combustion models. The results show that the peak pressure calculated by EBM is 2.78 % higher than the experimental value, and the peak pressure calculated by CFM is closest to the experimental value. The EBM is the most accurate for CO2 emissions. The deviation of CO emissions from the experimental values calculated by CTM is the smallest (< 1 %). The NOx emission calculated by CTM is good agreement with the experimental value at low speed, and EBM is in best agreement with the experimental value at high speed. The hydrocarbon emission calculated by CFM is the most accurate, with a deviation of less than 0.6 %. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis.

Author

Yan, Jin, Liao, Jianbin, Zhang, Weiwei, Dai, Jinliang, Huang, Chaoming, Li, Hanlin, and Yu, Hongliang

Subjects
SPECTROGRAMS, FAULT diagnosis
Abstract

In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features. [ABSTRACT FROM AUTHOR]

Published
2024
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