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3,703 results on '"Liu, Yiming"'

113. Outage Probability of Intelligent Reflecting Surfaces Assisted Full Duplex Two-way Communications

Author

Lu, Binyu, Wang, Rui, and Liu, Yiming

Subjects
Computer Science - Information Theory
Abstract

In this letter, we study the outage probability of intelligent reflecting surface (IRS) assisted full duplex two-way communication systems, which characterizes the performance of overcoming the transmitted data loss caused by long deep fades. To this end, we first derive the probability distribution of the cascaded end-to-end equivalent channel with an arbitrarily given IRS beamformer. Our analysis shows that deriving such probability distribution in the considered case is more challenging than the case with the phase-matched IRS beamformer. Then, with the derived probability distribution of the equivalent channel, we obtain the closed-form expression of the outage probability performance. It theoretically shows that the reflecting element number has a conspicuous effect on the improvement of the system reliability. Extensive numerical results verify the correctness of the derived results and confirm the superiority of the considered IRS assisted two-way communication system comparing to the one-way counterpart.

Published
2021

130. Analysis of Influencing Factors on the Teaching Reform Quality of Visual Communication Design Courses in Colleges and Universities Combined with Entropy Weighted TOPSIS Modeling

Author

Liu Yichang and Liu Yiming

Subjects
entropy weight topsis model, cipp model, assessment index system, visual communication design, 68m01, Mathematics, QA1-939
Abstract

In the fast-evolving field of information technology, visual communication design education must adapt through innovative curriculum reforms. This study employs the CIPP model to develop a robust index system for evaluating the effectiveness of these reforms within higher education institutions. Utilizing the entropy weight method for index weighting and the TOPSIS method for comprehensive evaluation, we assessed the reform quality across 12 colleges and universities. Our analysis, incorporating descriptive statistics and linear regression, reveals that a significant proportion (75%) of institutions score below 0.4, indicating average to low reform quality. Key factors impacting reform quality— teaching attitude, learning attitude, and course content—demonstrate substantial influence, with coefficients of 0.238, 0.648, and 0.658, respectively. All second-level factors showed a significant positive impact on reform quality, with significance coefficients below 0.01. These findings highlight the critical areas for enhancing curriculum reform efforts in visual communication design education.

Published
2024
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131. A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion

Author

Liu Yiming

Subjects
multi-feature fusion, textrank, tf-idf weights, location information weights, teaching resources classification, 68q05, Mathematics, QA1-939
Abstract

This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.

Published
2024
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132. Spectral Machine Learning for Pancreatic Mass Imaging Classification

Author

Liu, Yiming, Chen, Ying, Pan, Guangming, Wang, Weichung, Liao, Wei-Chih, Thian, Yee Liang, Chee, Cheng E., and Anastassiades, Constantinos P.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning, 62P10
Abstract

We present a novel spectral machine learning (SML) method in screening for pancreatic mass using CT imaging. Our algorithm is trained with approximately 30,000 images from 250 patients (50 patients with normal pancreas and 200 patients with abnormal pancreas findings) based on public data sources. A test accuracy of 94.6 percents was achieved in the out-of-sample diagnosis classification based on a total of approximately 15,000 images from 113 patients, whereby 26 out of 32 patients with normal pancreas and all 81 patients with abnormal pancreas findings were correctly diagnosed. SML is able to automatically choose fundamental images (on average 5 or 9 images for each patient) in the diagnosis classification and achieve the above mentioned accuracy. The computational time is 75 seconds for diagnosing 113 patients in a laptop with standard CPU running environment. Factors that influenced high performance of a well-designed integration of spectral learning and machine learning included: 1) use of eigenvectors corresponding to several of the largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose input attributes in classification training, taking into account only the fundamental information of the raw images with less noise; 2) removal of irrelevant pixels based on mean-level spectral test to lower the challenges of memory capacity and enhance computational efficiency while maintaining superior classification accuracy; 3) adoption of state-of-the-art machine learning classification, gradient boosting and random forest. Our methodology showcases practical utility and improved accuracy of image diagnosis in pancreatic mass screening in the era of AI., Comment: 17 pages, 3 figures

Published
2021

133. Advances and challenges in semantic communications: A systematic review

Author

Zhang Ping, Liu Yiming, Song Yile, and Zhang Jiaxiang

Subjects
semantic communications, semantic information, 6G, artificial intelligence, deep learning, Science, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Inspired by the recent success of machine learning (ML), the concept of semantic communication introduced by Weaver in 1949 has gained significant attention and has become a promising research direction. Unlike conventional communication systems, semantic communication emphasizes the precise retrieval of conveyed meaning from the source to the receiver, rather than focusing on the accurate transmission of symbols. Thus, semantic communication can achieve a significant gain in source data compression, alleviate communication bandwidth pressure, and support new intelligent services, which is envisioned as a crucial enabler of future sixth-generation (6G) networks. In this review, we critically summarize the advances made in semantic information and semantic communications, including theory, architecture, and potential applications. Moreover, we deeply explore the major challenges in developing semantic communications and present the development prospects, aiming to prompt further scientific and industrial advances in semantic communications.

Published
2023
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134. Joint Attention Mechanism of YOLOv5s for Coke Oven Smoke and Fire Recognition Algorithm

Author

Liu, Yiming, Zhang, Yunchu, Zhou, Yanfei, Zhang, Xinyi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Haijun, editor, Ke, Yinggen, editor, Wu, Zhou, editor, Hao, Tianyong, editor, Zhang, Zhao, editor, Meng, Weizhi, editor, and Mu, Yuanyuan, editor

Published
2023
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136. Model Based Development of Spacecraft OBDH Software

Author

Dong, Zhenhui, Zhang, Yahang, Yang, Peiyao, Liu, Yiming, Chu, Xuan, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Li, Ruidong, editor, Jia, Min, editor, and Taleb, Tarik, editor

Published
2023
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138. Research on the Design of Digital Village Construction Indicator System from the Perspective of Policy Tools : Shandong as an Example

Author

Li, Min, Li, Xiaohui, Li, Gang, Liu, Yiming, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Luqman, Adeel, editor, Li, Hongbo, editor, and Ali, Ghaffar, editor

Published
2023
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