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Xiaofei Zhang - IEEE Xplore Author Profile

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Autonomous driving technology has the potential to revolutionize the transportation industry. However, ensuring the safety of passengers and other traffic participants is a crucial challenge that must be addressed. One major concern is how the vehicle should react in emergency situations, where split-second decisions must be made to avoid collisions and minimize damage. This study presents a novel...Show More
Random vector functional ink (RVFL) networks belong to a class of single hidden layer neural networks in which some parameters are randomly selected. Their network structure in which contains the direct links between inputs and outputs is unique, and stability analysis and real-time performance are two difficulties of the control systems based on neural networks. In this paper, combining the advan...Show More
Adaptive dynamic programming (ADP) is a kind of intelligent control method, and it is a non-model-based method that can directly approximate the optimal control policy via online learning. The gradient algorithm is usually used to update weights of action networks and critic networks, however it is clear that gradient descent-based learning methods are generally very slow due to improper learning ...Show More
Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle (ADV). As is known, historical data collected from ADV contains valuable information about control systems, therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV an...Show More
In model free adaptive control (MFAC), a virtual equivalent dynamic linearized model is built. The linearization length constants (LLCs) of the virtual equivalent dynamic linearized model are selected by the practitioner based on experience. In this paper, the optimal LLCs are investigated, and compact model free adaptive control (CMFAC) is introduced for a class of unknown discrete-time nonlinear...Show More
In this paper, we propose a complete online learning framework for object detection system creatively. The framework efficiently combines Pixel Intensity Comparisons Organized in Decision Trees (PICO) and Local Receptive Fields Based Extreme Learning Machine with Online Sequential Learning Mechanism (OS-ELM-LRF). OS-ELM-LRF is the modified ELM-LRF for which we add the online sequential mechanism. ...Show More
With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller (SMC) with the exponential and p...Show More
Proportional control plays a role in stabilizing the system, and sliding mode variable structure control algorithm has strong robustness to external disturbances and the change of system parameters. The sliding mode controller (SMC) with proportional integral switching gain is given in order to weaken the chattering problem of sliding mode variable structure control, and the stability analysis is ...Show More
In natural spoken language there are many meaningless modal particles and dittographes, furthermore ASR (automatic speech recognition) often has some recognition errors and the ASR results have no punctuations. Therefore, the translation would be rather poor if the ASR results are directly translated by MT (machine translation). In this paper, an ASR normalization approach was introduced for machi...Show More
In cross-language information retrieval (CLIR), the query sentence is often combined with a series of query keywords, rather than a complete natural sentence. Lack of necessary contextual syntactic information in such a query sentence makes it impossible to achieve a unique translation of the query sentence with acceptable precision. In this paper, we convert the translation of query sentence to t...Show More
Conditional random fields (CRFs) for sequence labeling offer advantages over both generative models like hidden Markov model (HMM) and classifiers applied at each sequence position. First, the CRFs don't force to adhere to the independence assumption and thus can depend on arbitrary, non-independent features, without accounting for the distribution of those dependencies. Since CRFs models are able...Show More
The maximum entropy (ME) conditional models don't force to adhere to the independence assumption such as in Hidden Markov generative models, and thus the ME-based part-of-speech (POS) tagger can depend on arbitrary, non-independent features, which are benefit to the POS tagging, without accounting for the distribution of those dependencies. Since ME models are able to flexibly utilize a wide varie...Show More
In order to improve electromagnetic compatibility and deduce ripple of output voltage of the voltage-programmed buck converter, a state-feedback method with saw-tooth wave function to make this circuit system chaotic are proposed. The effectiveness and correction are proved by numerical simulation. Furthermore, in order to analyze physics mechanics from theory about reducing ripple of output volta...Show More
As a classical statistical pattern recognition algorithm characterized with high accuracy and stability, KNN has been used widely in text categorization. But since KNNpsilas time complexity is directly proportional to the sample size, its classification speed is very slow. In this paper, we propose a new KNN text categorization algorithm based on semantic centre, which we call SKNN, to speed up th...Show More