3篇
您的检索式：作者名="Yangdong Deng"
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| 1 | A fingerprint feature extraction algorithm based on curvature of Bezier curve显示文摘Fingerprint feature extraction is a key step of fingerprint identification. A novel feature extraction algorithm is proposed in this paper, which describes fingerprint feature with the bending information of fingerprint ridges. Ridges in the specific region of fingerprint images are traced firstly in the algorithm, and then these ridges are fit with Bezier curve. Finally, the point that has the maximal curvature on Bezier curve is defined as a feature point. Experimental results demonstrate that this kind of feature points characterize the bending trend of fingerprint ridges effectively, and they are robust to noise, in addition, the extraction precision of this algorithm is also better than the conventional approaches. | Yuan Huaqiang Ye Yangdong Deng Jianguang Chai Xiaoguang Li Yong | 2007 | Progress in Natural Science:Materials International2007，17，11： | 5 |
| 2 | A Hierarchical Ensemble Learning Framework for Energy-Efficient Automatic Train Driving显示文摘Railway transportation plays an important role in modern society. As China's massive railway transportation network continues to grow in total mileage and operation density, the energy consumption of trains becomes a serious concern. For any given route, the geographic characteristics are known a priori, but the parameters(e.g., loading and marshaling) of trains vary from one trip to another. An extensive analysis of the train operation data suggests that the control gear operation of trains is the most important factor that affects the energy consumption. Such an observation determines that the problem of energy-efficient train driving has to be addressed by considering both the geographic information and the trip parameters. However, the problem is difficult to solve due to its high dimension, nonlinearity, complex constraints, and time-varying characteristics. Faced with these difficulties, we propose an energy-efficient train control framework based on a hierarchical ensemble learning approach. Through hierarchical refinement, we learn prediction models of speed and gear. The learned models can be used to derive optimized driving operations under real-time requirements. This study uses random forest and bagging – REPTree as classification algorithm and regression algorithm, respectively. We conduct an extensive study on the potential of bagging, decision trees, random forest, and feature selection to design an effective hierarchical ensemble learning framework. The proposed framework was testified through simulation. The average energy consumption of the proposed method is over 7% lower than that of human drivers. | Guohua Xi Xibin Zhao Yan Liu Jin Huang Yangdong Deng | 2019 | Tsinghua Science and Technology2019，24，2： | 3 |
| 3 | Deadlock Detection in FPGA Design: A Practical Approach显示文摘Formal verification of VHSIC Hardware Description Language(VHDL) in Field-Programmable Gate Array(FPGA) design has been discussed for many years. In this paper we provide a practical approach to do so. We present a semi-automatic way to verify FPGA VHDL software deadlocks, especially those that reside in automata.A domain is defined to represent the VHDL modules that will be verified; these modules will be transformed into Verilog models and be verified by SMV tools. By analyzing the verification results of SMV, deadlocks can be found;after looking back to the VHDL code, the deadlocking code is located and the problem is solved. VHDL verification is particularly important in safety-critical software. As an example, our solution is applied to a Multifunction Vehicle Bus Controller(MVBC) system for a train. The safety properties were tested well in the development stage, but experienced a breakdown during the long-term software testing stage, which was mainly caused by deadlocks in the VHDL software. In this special case, we managed to locate the VHDL deadlocks and solve the problem by the FPGA deadlock detection approach provided in this paper, which demonstrates that our solution works well. | Dexi Wang Fei He Yangdong Deng Chao Su Ming Gu Jiaguang Sun | 2015 | Tsinghua Science and Technology2015，20，2： | 0 |
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