Browsing by Author "Wang, Fei-Yue"

Now showing 1 - 7 of 7
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    AI in game intelligence—from multi-role game to parallel game
    (2020-09) Shen, Yu; Han, Jinpeng; Li, Lingxi; Wang, Fei-Yue; Electrical and Computer Engineering, School of Engineering and Technology
    The domestic and overseas research progress of artificial intelligence technology in the field of games was summarized and the significance of the research progress in the field of games for real life was analyzed.In view of the gap between simulation and reality in model based methods and the lack of generality of the model-based approach in reinforcement learning,the idea and method of parallel game were put forward,and the advance of parallel game in solving the existing problems of single-role game and multi-role game was introduced.The parallel game method will be the cornerstone of the general artificial intelligence.
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    Detecting Traffic Information From Social Media Texts With Deep Learning Approaches
    (IEEE, 2018-11) Chen, Yuanyuan; Lv, Yisheng; Wang, Xiao; Li, Lingxi; Wang, Fei-Yue; Electrical and Computer Engineering, School of Engineering and Technology
    Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.
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    From Software-Defined Vehicles to Self-Driving Vehicles: A Report on CPSS-Based Parallel Driving
    (IEEE, 2018-10) Han, Shuangshuang; Cao, Dongpu; Li, Li; Li, Lingxi; Li, Shengbo Eben; Zheng, Nan-Ning; Wang, Fei-Yue; Mechanical and Energy Engineering, School of Engineering and Technology
    On June 11th, 2017, the 28th IEEE Intelligent Vehicles Symposium (IV'2017) was held in Redondo Beach, California, USA. As one of the 8 workshops at IV'2017, the cyber-physical-social systems (CPSS)-based parallel driving (WS'08), organized by the State Key Laboratory for Management and Control of Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences, China, Xi'an Jiaotong University, China, Tsinghua University, China, Indiana University-Purdue University Indianapolis, USA, and Cranfield University, U.K, has attracted both researchers and practitioners in intelligent vehicles. About 60-70 participants from various countries had extensive and deep discussions on definition, challenges and alternative solutions for CPSS-based parallel driving, and widely agreed that it is a novel paradigm of cloud-based automated driving technologies. Six speakers shared their ideas, studies, field applications, and vision for future along these emerging directions from software-defined vehicles to self-driving vehicles.
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    Parallel Mining Operating Systems: From Digital Twins to Mining Intelligence
    (IEEE Xplore, 2021-07) Chen, Long; Long, Xiaoming; Wang, Ge; Cao, Dongpu; Li, Lingxi; Wang, Fei-Yue; Electrical and Computer Engineering, School of Engineering and Technology
    With the rapid development and modernization requirement of global coal industry, there is an emerging need for intelligent and unmanned mining systems. In this paper, the Intelligent Mining Operating System (IMOS) is proposed and developed, based on the parallel management and control of mining operating infrastructure that integrates the intelligent mining theory, the ACP-based (Artificial societies, Computational experiments, Parallel execution) parallel intelligence approaches, and the new generation of artificial intelligence (AI) technologies. To satisfy the intelligent and unmanned demand of open-pit mines, the IMOS architecture is developed by integrating the theory of digital quadruplets. The main subsystems and functions of IMOS are elaborated in detail, including a single-vehicle operating subsystem, multi-vehicle collaboration subsystem, vehicle-road collaboration subsystem, unmanned intelligent subsystem, dispatch management subsystem, parallel management and control subsystem, supervisory subsystem, remote takeover subsystem, and communication subsystem. The IMOS presented in this paper is the first integrated solution for intelligent and unmanned mines in China, and has been implemented over ten main open pits in the past few years. Its deployment and utilization will effectively improve the production efficiency and safety level of open-pit mines, promote the construction of ecological mines, and bring great significance to the realization of sustainable mining development.
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    Pedestrian Detection based on Clustered Poselet Models and Hierarchical And-Or Grammar
    (IEEE, 2015-04) Li, Bo; Chen, Yaobin; Wang, Fei-Yue; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    In this paper, a novel part-based pedestrian detection algorithm is proposed for complex traffic surveillance environments. To capture posture and articulation variations of pedestrians, we define a hierarchical grammar model with the and-or graphical structure to represent the decomposition of pedestrians. Thus, pedestrian detection is converted to a parsing problem. Next, we propose clustered poselet models, which use the affinity propagation clustering algorithm to automatically select representative pedestrian part patterns in keypoint space. Trained clustered poselets are utilized as the terminal part models in the grammar model. Finally, after all clustered poselet activations in the input image are detected, one bottom-up inference is performed to effectively search maximum a posteriori (MAP) solutions in the grammar model. Thus, consistent poselet activations are combined into pedestrian hypotheses, and their bounding boxes are predicted. Both appearance scores and geometry constraints among pedestrian parts are considered in inference. A series of experiments is conducted on images, both from the public TUD-Pedestrian data set and collected in real traffic crossing scenarios. The experimental results demonstrate that our algorithm outperforms other successful approaches with high reliability and robustness in complex environments.
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    A Situation-Aware Collision Avoidance Strategy for Car-Following
    (IEEE, 2018-07) Li, Li; Peng, Xinyu; Wang, Fei-Yue; Cao, Dongpu; Mechanical and Energy Engineering, School of Engineering and Technology
    In this paper, we discuss how to develop an appropriate collision avoidance strategy for car-following. This strategy aims to keep a good balance between traffic safety and efficiency while also taking into consideration the unavoidable uncertainty of position/speed perception/measurement of vehicles and other drivers. Both theoretical analysis and numerical testing results are provided to show the effectiveness of the proposed strategy.
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    Steps toward Parallel Intelligence
    (IEEE, 2016-10) Wang, Fei-Yue; Wang, Xiao; Li, Lingxi; Li, Li; Department of Electrical and Computer Engineering, School of Engineering and Technology
    The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the "new normal" of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS.