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您的检索式：作者名="Zhicong Qiu"
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| 1 | Experimental Investigation on Macro Spray Characteristics of Octanol-Biodiesel-Diesel Ternary Fuel Blend显示文摘This study investigates the spray characteristics of ternary blends composed of octanol, biodiesel, and diesel fuel.Experiments are conducted using six materials to examine the variation in spray characteristic and to verify and compare a previously established spray tip penetration model with a modified model. The results show that the addition of OB100(30%of octanol, 70% of biodiesel) improves the spray characteristics of the fuel. Specifically, the addition of 10% or 20% of OB100 leads to a slight increase in the spray tip penetration, average spray cone angle, maximum spray width, and the spray area of the fuel blend;however, further addition of OB100 causes a corresponding decrease in these parameters. Based on previous research, this study uses kinematic viscosity instead of dynamic viscosity and density to modify the prediction model of spray tip penetration. The modified model exhibits a better fit quality and agreement with the experimental data,making it more suitable for predicting the spray tip penetration of fuel blends compared to the Hiroyasu-Arai model. | Tian Junjian Qiu Zhicong Zhao Lifei Li Fengyu Hu Peng Lin Qizhao | 2023 | China Petroleum Processing & Petrochemical Technology2023，25，4： | 0 |
| 2 | Instance Reweighting Adversarial Training Based on Confused Label显示文摘Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts. | Zhicong Qiu Xianmin Wang Huawei Ma Songcao Hou Jing Li Zuoyong Li | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
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