Your search for: 'author:("Li, Junwei")' has returned 4 results. (0.012s) Save search

Authors: Li, Junwei | Liu, Huanyu | Jin, Yong | Zhao, Aoxiang

Article Type: Research Article

Abstract: Research on conflict evidence fusion is an important topic of evidence theory. When fusing conflicting evidence, Dempster-Shafer evidence theory sometimes produces counter-intuitive results. Thus, this work proposes a conflict evidence fusion method based on improved conflict coefficient and belief entropy. Firstly, the proposed method uses an improved conflict coefficient to measure the degree of conflict, and the conflict matrix is constructed to get the support degree of evidence. Secondly, in order to measure the uncertainty of evidence, an improved belief entropy is proposed, and the information volume of evidence is obtained by the improve entropy. Next, connecting with the support …degree and information volume, We get the weight coefficient, and use it to modify the evidence. Finally, using the combination rule of Dempster for fusion. Simulation experiments have demonstrated the effectiveness and superiority of the proposed method in this paper. Show more

Keywords: Evidence theory, conflict evidence, conflict coefficient, beleief entropy, combination rule

DOI: 10.3233/JIFS-221507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1973-1984, 2024

Authors: Li, Junwei | Lian, Mengmeng | Jin, Yong | Xia, Miaomiao | Hou, Huaibin

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434 .

DOI: 10.3233/JIFS-235593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9465-9478, 2024

Authors: Chen, Xiaodong | Li, Junwei | Zhang, Zhaoxia | Wang, Yi | Jia, Zhongwei | Pu, Kui | Yu, Daoyin

Article Type: Research Article

Abstract: BACKGROUND: Coronary stenosis is the main cause of the coronary heart disease (CHD). However, coronary arteriography (CAG), which is considered as the 'gold standard' of determining the location and severity of CHD, hardly acquires a satisfactory image for some lesions by traditional viewing angles. OBJECTIVE: We proposed a new approach to calculate the optimal viewing angles of CAG system to observe vessel segment of interest. METHODS: Firstly, the 4-D coronary arteries are segmented to obtain a dynamic vessel model. Then, a "rendering" method in computer graphics is used to calculate the optimal viewing angles of the vessel segment in the …entire cardiac cycle. At last, an intersection of these angles can be regarded as the optimal ones in the whole cardiac cycle. RESULTS: Within the constraint of 2% foreshortening, the single phase data show 1% foreshortening without overlapping at the optimal angles proposed by our method, compared with 1.8% foreshortening at working angles set by clinical experts. And the multi-phase experiments also have good results. CONCLUSIONS: The new approach can provide doctors optimal viewing angles of interested vessel segment in the whole cardiac cycle. Show more

Keywords: 4-D cardiac CT, coronary angiography (CAG), 3-D coronary segmentation, optimal viewing angles

DOI: 10.3233/XST-140415

Citation: Journal of X-Ray Science and Technology, vol. 22, no. 2, pp. 137-145, 2014

Authors: Xiong, Haoyu | Yang, Leixin | Fang, Gang | Li, Junwei | Xiang, Yu | Zhang, Yaping

Article Type: Research Article

Abstract: Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a …novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models. Show more

Keywords: Test-time augmentation, test-time robustification, text classification, language model, anomaly detection

DOI: 10.3233/JIFS-236010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8783-8798, 2024